The University of Queensland

The EM algorithm and extensions

McLachlan, Geoffrey J. and Krishnan, Thriyambakam (2008). The EM algorithm and extensions. 2nd ed. Hoboken, NJ, United States: John Wiley & Sons. doi: 10.1002/9780470191613

Analyzing Microarray Gene Expression Data

McLachlan, G. J., Do, K. and Ambroise, C (2004). Analyzing Microarray Gene Expression Data. New York: Wiley-Interscience.

Analyzing microarray gene expression data

McLachlan, Geoffrey J., Do, Kim-Anh and Ambroise, Christophe (2004). Analyzing microarray gene expression data. Hoboken, NJ, USA: John Wiley & Sons. doi: 10.1002/047172842x

Finite Mixture Models

McLachlan, G. J. and Peel, D. (2000). Finite Mixture Models. New York: John Wiley & Sons.

Finite mixture models: McLachlan/finite mixture models

McLachlan, Geoffrey and Peel, David (2000). Finite mixture models: McLachlan/finite mixture models. Hoboken, NJ, USA: John Wiley & Sons. doi: 10.1002/0471721182

The EM algorithm and extensions

McLachlan, Geoffrey J. and Krishnan, Thriyambakam (1997). The EM algorithm and extensions. New York, United States: Wiley.

Discriminant analysis and statistical pattern recognition

McLachlan, Geoffrey J. (1992). Discriminant analysis and statistical pattern recognition. Hoboken, NJ, USA: John Wiley & Sons. doi: 10.1002/0471725293

Discriminant analysis and statistical pattern recognition

McLachlan, Geoffrey John (1992). Discriminant analysis and statistical pattern recognition. New York , United States: Wiley.

Mixture models : inference and applications to clustering

McLachlan, Geoffrey J. and Basford, Kaye E. (1988). Mixture models : inference and applications to clustering. New York, United States: Marcel Dekker.

On mean and/or variance mixtures of normal distributions

Lee, S.X. and McLachlan, G.J. (2021). On mean and/or variance mixtures of normal distributions. Statistical learning and modeling in data analysis: methods and applications. (pp. 1-10) edited by S. Balzano, G.C. Porzio, R. Salvatore, D. Vistocco and M. Vichi. Cham, Switzerland: Springer. doi: 10.1007/978-3-030-69944-4_13

Automated gating and dimension reduction of high-dimensional cytometry data

Lee, Sharon X., McLachlan, Geoffrey J. and Pyne, Saumyadipta (2021). Automated gating and dimension reduction of high-dimensional cytometry data. Mathematical, computational and experimental T cell immunology. (pp. 281-294) edited by Carmen Molina-París and Grant Lythe . Cham, Switzerland: Springer. doi: 10.1007/978-3-030-57204-4_16

Estimation of classification rules from partially classified data

McLachlan, Geoffrey and Ahfock, Daniel (2021). Estimation of classification rules from partially classified data. Data analysis and rationality in a complex world. (pp. 149-157) edited by Theodore Chadjipadelis, Berthold Lausen, Angelos Markos, Tae Rim Lee, Angela Montanari and Rebecca Nugent. Cham, Switzerland: Springer. doi: 10.1007/978-3-030-60104-1_17

Comprehensive chemometrics: chemical and biochemical data analysis

McLachlan, G. J., Rathnayake, S. and Lee, S. X. (2020). Comprehensive chemometrics: chemical and biochemical data analysis. Comprehensive chemometrics: chemical and biochemical data analysis. (pp. 267-304) edited by Steven Brown, Roma Tauler and Beata Walczak. Oxford, United Kingdom: Elsevier.

Mixture of factor analyzers for the clustering and visualization of high-dimensional data

McLachlan, Geoffrey J., Baek, Jangsun and Rathnayake, Suren I. (2019). Mixture of factor analyzers for the clustering and visualization of high-dimensional data. Advances in latent class analysis: a festschrift in honor of C. Mitchell Dayton. (pp. 79-98) edited by Gregory R. Hancock, Jeffrey R. Harring and George B. Macready. Charlotte, NC, United States: Information Age Publishing.

Risk measures based on multivariate skew normal and skew t-mixture models

Lee, Sharon X. and McLachlan, Geoffrey J. (2018). Risk measures based on multivariate skew normal and skew t-mixture models. Asymmetric dependence in finance: diversification, correlation and portfolio management in market downturns. (pp. 152-168) edited by Jamie Alcock and Stephen Satchell. Chichester, West Sussex, United Kingdom: John Wiley & Sons. doi: 10.1002/9781119288992.ch7

Clustering

McLachlan, G. J., Bean, R. W. and Ng, S. K. (2017). Clustering. Bioinformatics Vol. II: Structure, Function, and Applications. (pp. 345-362) edited by Jonathan M. Keith. New York, NY, United States: Humana Press. doi: 10.1007/978-1-4939-6613-4_19

Finite mixture models in biostatistics

Lee, Sharon X., Ng, Shu-Kay and McLachlan, Geoffrey J. (2017). Finite mixture models in biostatistics. Disease Modelling and Public Health, Part A. (pp. 75-102) edited by Arni S.R. Srinivasa Rao, Saumyadipta Pyne and C.R. Rao. Amsterdam, Netherlands: Elsevier. doi: 10.1016/bs.host.2017.08.005

On the identification of correlated differential features for supervised classification of high-dimensional data

Ng, Shu Kay and McLachlan, Geoffrey J. (2017). On the identification of correlated differential features for supervised classification of high-dimensional data. Data science, innovative developments in data analysis and clustering. (pp. 43-57) edited by Francesco Palumbo, Angela Montanari and Maurizio Vichi. Cham, Switzerland: Springer. doi: 10.1007/978-3-319-55723-6

Statistical evaluation of labeled comparative profiling proteomics experiments using permutation test

Nguyen, Hien D., McLachlan, Geoffrey J. and Hill, Michelle M. (2017). Statistical evaluation of labeled comparative profiling proteomics experiments using permutation test. Proteome bioinformatics. (pp. 109-117) edited by Shivakumar Keerthikumar and Suresh Mathivanan. New York, NY United States: Humana Press. doi: 10.1007/978-1-4939-6740-7_9

Application of mixture models to large datasets

Lee, Sharon X., McLachlan, Geoffrey J. and Pyne, Saumyadipta (2016). Application of mixture models to large datasets. Big data analytics: methods and applications. (pp. 57-74) edited by Saumyadipta Pyne, B. L. S. Prakasa Rao and S. B. Rao. New Delhi, India: Springer India. doi: 10.1007/978-81-322-3628-3_4

Mixture distributions - further developments

McLachlan, Geoffrey J. (2016). Mixture distributions - further developments. Wiley statsref: statistics reference online. (pp. 1-13) Chichester, United Kingdom: John Wiley & Sons. doi: 10.1002/9781118445112.stat00947.pub2

Mixture models for standard p-dimensional Euclidean data

McLachlan, Geoffrey J. and Rathnayake, Suren I. (2016). Mixture models for standard p-dimensional Euclidean data. Handbook of cluster analysis. (pp. 145-171) edited by Christian Hennig, Marina Meila, Fionn Murtagh and Roberto Rocci. Boca Raton, FL, United States: CRC Press. doi: 10.1201/b19706-14

Computation: Expectation-Maximization Algorithm

McLachlan, Geoffrey J. (2015). Computation: Expectation-Maximization Algorithm. International Encyclopedia of the Social & Behavioral Sciences: Second Edition. (pp. 469-474) Amsterdam, Netherlands: Elsevier . doi: 10.1016/B978-0-08-097086-8.42007-6

Mixture Models in Statistics

McLachlan, Geoffrey J. (2015). Mixture Models in Statistics. International Encyclopedia of the Social & Behavioral Sciences: Second Edition. (pp. 624-628) Amsterdam, Netherlands: Elsevier . doi: 10.1016/B978-0-08-097086-8.42055-6

Multivariate Analysis: Classification and Discrimination

McLachlan, Geoffrey (2015). Multivariate Analysis: Classification and Discrimination. International Encyclopedia of the Social & Behavioral Sciences: Second Edition. (pp. 116-120) Amsterdam, Netherlands: Elsevier . doi: 10.1016/B978-0-08-097086-8.42150-1

Clustering of gene expression data via normal mixture models

McLachlan, G. J., Flack, L. K., Ng, S. K. and Wang, K. (2013). Clustering of gene expression data via normal mixture models. Statistical methods for microarray data analysis: methods and protocols. (pp. 103-119) edited by Andrei Y. Yakovlev, Lev Klebanov and Daniel Gaile. New York, NY, United States: Humana Press. doi: 10.1007/978-1-60327-337-4_7

An enduring interest in classification: supervised and unsupervised

McLachlan, G. J. (2012). An enduring interest in classification: supervised and unsupervised. Journeys to data mining: experiences from 15 renowned researchers. (pp. 147-171) edited by Mohamed Medhat Gaber. Heidelberg, Germany: Springer. doi: 10.1007/978-3-642-28047-4_12

The EM algorithm

Ng, Shu Kay, Krishnan, Thriyambakam and McLachlan, Geoffrey J. (2012). The EM algorithm. Handbook of Computational Statistics: Concepts and Methods. (pp. 139-172) edited by James E. Gentle, Wolfgang Karl Hardle and Yuichi Mori. Berlin & New York: Springer. doi: 10.1007/978-3-642-21551-3__6

The EM Algorithm

Ng, Shu Kay, Krishnan, Thriyambakam and McLachlan, Geoffrey J. (2011). The EM Algorithm. Handbook of Computational Statistics. (pp. 139-172) Berlin, Germany: Springer. doi: 10.1007/978-3-642-21551-3_6

Mixtures of factor analyzers for the analysis of high-dimensional data

McLachlan, Geoffrey J., Baek, Jangsun and Rathnayake, Suren I. (2011). Mixtures of factor analyzers for the analysis of high-dimensional data. Mixture estimation and applications. (pp. 189-212) edited by Kerrie L. Mengersen, Christian P. Robert and D. Michael Titterington. Chichester, United Kingdom: John Wiley and Sons. doi: 10.1002/9781119995678.ch9

Clustering of high-dimensional and correlated data

McLachlan, Geoffrey J., Ng, Shu-Kay and Wang, K. (2010). Clustering of high-dimensional and correlated data. Data Analysis and Classification: Proceedings of the 6th Conference of the Classification and Data Analysis Group of the SocietàItaliana di Statistica, Macerata, Italy 12-14 September, 2007. (pp. 3-11) edited by Francesco Palumbo, Carlo Natale Lauro and Michael J. Greenacre. Berlin; Heidelberg, Germany: Springer - Verlag. doi: 10.1007/978-3-642-03739-9_1

Clustering of high-dimensional data via finite mixture models

McLachlan, Geoff J. and Baek, Jangsun (2010). Clustering of high-dimensional data via finite mixture models. Advances in Data Analysis, Business Intelligence: Proceedings of the 32nd Annual Conference of the Gesellschaft für Klassifikation e.V., Joint Conference with the British Classification Society (BCS) and the Dutch/Flemish Classification Society (VOC Helmut-Schmidt-University, Hamburg, July 16–18, 2008. (pp. 33-44) edited by Andreas Fink, Berthold Lausen, Wilfried Seidel and Alfred Ultsch. Heidelberg, Germany: Springer-Verlag. doi: 10.1007/978-3-642-01044-6

Expert networks with mixed continuous and categorical feature variables: A location modeling approach.

Ng, Shu-Kay and McLachlan, Geoffrey J. (2010). Expert networks with mixed continuous and categorical feature variables: A location modeling approach.. Machine learning research progress. (pp. 355-368) edited by Hannah Peters and Mia Vogel. New York, U.S.A.: Nova Science.

Use of mixture models in multiple hypothesis testing with applications in bioinformatics

McLachlan, Geoffrey J. and Wockner, Leesa (2010). Use of mixture models in multiple hypothesis testing with applications in bioinformatics. Classification as a Tool for Research: Proceedings of the 11th IFCS Biennial Conference and 33rd Annual Conference of the Gesellschaft für Klassifikation. (pp. 177-184) edited by Hermann Locarek-Junge and Claus Weihs. Heidelberg, Germany: Springer-Verlag. doi: 10.1007/978-3-642-10745-0

Clustering methods for gene-expression data

Flack, L. K. and McLachlan, G. J. (2009). Clustering methods for gene-expression data. Handbook of Research on Systems Biology Applications in Medicine. (pp. 209-220) edited by Andriani Daskalaki. United States: IGI Global. doi: 10.4018/978-1-60566-076-9.ch011

EM

McLachlan, G. J. and Ng, S-K. (2009). EM. The Top Ten Algorithms in Data Mining. (pp. 93-115) edited by Wu, X. and Kumar, V.. Florida, United States: Chapman & Hall/CRC. doi: 10.1201/9781420089653-12

Model-based clustering

McLachlan, G. J. (2009). Model-based clustering. Comprehensive chemometrics: chemical and biochemical data analysis. (pp. 655-681) edited by Steven D. Brown, Roma Tauler and Beata Walczak. Oxford, U.K.: Elsevier Science. doi: 10.1016/B978-044452701-1.00068-5

Statistical analysis on microarray data: selection of gene prognosis signatures

Le Cao, Kim-Anh and McLachlan, Geoffrey J. (2009). Statistical analysis on microarray data: selection of gene prognosis signatures. Computational biology: issues and applications in oncology. (pp. 55-76) edited by Tuan Pham. New York, United States: Springer. doi: 10.1007/978-1-4419-0811-7_3

Clustering

McLachlan, G. J., Bean, R. W. and Ng, S.-K. (2008). Clustering. Bioinformatics, volume 2: Structure, function and applications. (pp. 423-439) edited by J. M. Keith. New Jersey, United States: Humana Press. doi: 10.1007/978-1-60327-429-6_22

Clustering of microarray data via mixture models

McLachlan, Geoffrey J., Ng, Angus and Bean, Richard W. (2008). Clustering of microarray data via mixture models. Statistical advances in the biomedical sciences: clinical trials, epidemiology, survival analysis, and bioinformatics. (pp. 365-383) edited by Atanu Biswas, Sujay Datta, Jason P. Fine and Mark R. Segal. Hoboken, NJ, United States: John Wiley & Sons. doi: 10.1002/9780470181218.ch21

Correcting for Selection Bias via Cross-Validation in the Classification of Microarray Data

McLachlan, G J., Chevelu, J. and Zhu, J. (2008). Correcting for Selection Bias via Cross-Validation in the Classification of Microarray Data. Beyond Parametrics in Interdisciplinary Research: Festschrift in Honor of Professor Pranab K. Sen. (pp. 364-376) edited by Balakrishnan, N., Pena, E. A. and Silvapulle, M. J.. United States: Institute of Mathematical Statistics. doi: 10.1214/193940307000000284

Use of microarray data via model-based classification in the study and prediction of survival from lung cancer

Jones, L., Ng, S., Ambroise, C, Monico, K. A., Khan, N. and McLachlan, G. J. (2005). Use of microarray data via model-based classification in the study and prediction of survival from lung cancer. Methods of microarray data analysis IV. (pp. 163-173) edited by Jennifer S. Shoemaker and Simon M. Lin. New York, USA: Springer. doi: 10.1007/0-387-23077-7_13

The EM algorithm

Ng, S. K., Krishnan, T. and McLachlan, G. J. (2004). The EM algorithm. Handbook of Computational Statistics: Concepts and Methods. (pp. 137-168) edited by J.E. Gentle, W. Hardle and Y. Mori. Germany: Springer-Verlag.

On clustering by mixture models

McLachlan, G. J., Ng, A.S. K. and Peel, D. (2003). On clustering by mixture models. Exploratory Data Analysis in Empirical Research. (pp. 141-148) edited by M. Schwaiger and O. Opitz. Germany: Springer. doi: 10.1007/978-3-642-55721-7_16

A new algorithm for support vector regression with automatic selection of hyperparameters

Wang, You-Gan, Wu, Jinran, Hu, Zhi-Hua and McLachlan, Geoffrey J. (2023). A new algorithm for support vector regression with automatic selection of hyperparameters. Pattern Recognition, 133 108989, 1-9. doi: 10.1016/j.patcog.2022.108989

An Automated Machine learning (AutoML) approach to regression models in minerals processing with case studies of developing industrial comminution and flotation models

Koh, Edwin J. Y., Amini, Eiman, Gaur, Shruti, Becerra Maquieira, Miguel, Jara Heck, Christian, McLachlan, Geoffrey J. and Beaton, Nick (2022). An Automated Machine learning (AutoML) approach to regression models in minerals processing with case studies of developing industrial comminution and flotation models. Minerals Engineering, 189 107886, 107886. doi: 10.1016/j.mineng.2022.107886

Order selection with confidence for finite mixture models

Nguyen, Hien D., Fryer, Daniel and McLachlan, Geoffrey J. (2022). Order selection with confidence for finite mixture models. Journal of the Korean Statistical Society, 52 (1), 154-184. doi: 10.1007/s42952-022-00195-z

A spatial heterogeneity mixed model with skew-elliptical distributions

Farzammehr, Mohadeseh Alsadat and McLachlan, Geoffrey J. (2022). A spatial heterogeneity mixed model with skew-elliptical distributions. Communications for Statistical Applications and Methods, 29 (3), 373-391. doi: 10.29220/csam.2022.29.3.373

Approximation of probability density functions via location-scale finite mixtures in Lebesgue spaces

Nguyen, TrungTin, Chamroukhi, Faicel, Nguyen, Hien D. and McLachlan, Geoffrey J. (2022). Approximation of probability density functions via location-scale finite mixtures in Lebesgue spaces. Communications in Statistics - Theory and Methods, 52 (14), 1-12. doi: 10.1080/03610926.2021.2002360

Statistical file-matching of non-Gaussian data: a game theoretic approach

Ahfock, Daniel, Pyne, Saumyadipta and McLachlan, Geoffrey J. (2022). Statistical file-matching of non-Gaussian data: a game theoretic approach. Computational Statistics and Data Analysis, 168 107387, 1-16. doi: 10.1016/j.csda.2021.107387

Semi-supervised learning of classifiers from a statistical perspective: a brief review

Ahfock, Daniel and McLachlan, Geoffrey J. (2022). Semi-supervised learning of classifiers from a statistical perspective: a brief review. Econometrics and Statistics, 26, 124-138. doi: 10.1016/j.ecosta.2022.03.007

An overview of skew distributions in model-based clustering

Lee, Sharon X. and McLachlan, Geoffrey J. (2022). An overview of skew distributions in model-based clustering. Journal of Multivariate Analysis, 188 104853, 1-14. doi: 10.1016/j.jmva.2021.104853

Approximations of conditional probability density functions in Lebesgue spaces via mixture of experts models

Nguyen, Hien Duy, Nguyen, TrungTin, Chamroukhi, Faicel and McLachlan, Geoffrey John (2021). Approximations of conditional probability density functions in Lebesgue spaces via mixture of experts models. Journal of Statistical Distributions and Applications, 8 (1) 13. doi: 10.1186/s40488-021-00125-0

Joint frailty modeling of time-to-event data to elicit the evolution pathway of events: a generalized linear mixed model approach

Ng, Shu Kay, Tawiah, Richard, McLachlan, Geoffrey J. and Gopalan, Vinod (2021). Joint frailty modeling of time-to-event data to elicit the evolution pathway of events: a generalized linear mixed model approach. Biostatistics, 24 (1), 108-123. doi: 10.1093/biostatistics/kxab037

Utilising convolutional neural networks to perform fast automated modal mineralogy analysis for thin-section optical microscopy

Koh, Edwin J. Y., Amini, Eiman, McLachlan, Geoffrey J. and Beaton, Nick (2021). Utilising convolutional neural networks to perform fast automated modal mineralogy analysis for thin-section optical microscopy. Minerals Engineering, 173 107230, 107230. doi: 10.1016/j.mineng.2021.107230

Robust clustering based on finite mixture of multivariate fragmental distributions

Maleki, Mohsen, McLachlan, Geoffrey J. and Lee, Sharon X. (2021). Robust clustering based on finite mixture of multivariate fragmental distributions. Statistical Modelling, 23 (3), 1-26. doi: 10.1177/1471082X211048660

Utilising a deep neural network as a surrogate model to approximate phenomenological models of a comminution circuit for faster simulations

Koh, Edwin J.Y., Amini, Eiman, McLachlan, Geoffrey J. and Beaton, Nick (2021). Utilising a deep neural network as a surrogate model to approximate phenomenological models of a comminution circuit for faster simulations. Minerals Engineering, 170 107026, 1-11. doi: 10.1016/j.mineng.2021.107026

Data fusion using factor analysis and low-rank matrix completion

Ahfock, Daniel, Pyne, Saumyadipta and McLachlan, Geoffrey J. (2021). Data fusion using factor analysis and low-rank matrix completion. Statistics and Computing, 31 (5) 58. doi: 10.1007/s11222-021-10033-7

Multi‐node expectation–maximization algorithm for finite mixture models

Lee, Sharon X., McLachlan, Geoffrey J. and Leemaqz, Kaleb L. (2021). Multi‐node expectation–maximization algorithm for finite mixture models. Statistical Analysis and Data Mining: The ASA Data Science Journal, 14 (4) sam.11529, 297-304. doi: 10.1002/sam.11529

Bayesian analysis of generalized linear mixed models with spatial correlated and unrestricted skew normal errors

Farzammehr, M. A., Mohammadzadeh, M, Zadkarami, M. R. and McLachlan, G. J. (2021). Bayesian analysis of generalized linear mixed models with spatial correlated and unrestricted skew normal errors. Communications in Statistics: Theory and Methods, 51 (24), 1-22. doi: 10.1080/03610926.2021.1897843

Harmless label noise and informative soft-labels in supervised classification

Ahfock, Daniel and McLachlan, Geoffrey J. (2021). Harmless label noise and informative soft-labels in supervised classification. Computational Statistics and Data Analysis, 161 107253, 107253. doi: 10.1016/j.csda.2021.107253

On formulations of skew factor models: Skew factors and/or skew errors

Lee, Sharon X. and McLachlan, Geoffrey J. (2021). On formulations of skew factor models: Skew factors and/or skew errors. Statistics and Probability Letters, 168 108935, 108935. doi: 10.1016/j.spl.2020.108935

An apparent paradox: a classifier based on a partially classified sample may have smaller expected error rate than that if the sample were completely classified

Ahfock, Daniel and McLachlan, Geoffrey J. (2020). An apparent paradox: a classifier based on a partially classified sample may have smaller expected error rate than that if the sample were completely classified. Statistics and Computing, 30 (6), 1779-1790. doi: 10.1007/s11222-020-09971-5

Mixtures of factor analyzers with scale mixtures of fundamental skew normal distributions

Lee, Sharon X., Lin, Tsung-I and McLachlan, Geoffrey J. (2020). Mixtures of factor analyzers with scale mixtures of fundamental skew normal distributions. Advances in Data Analysis and Classification, 15 (2), 481-512. doi: 10.1007/s11634-020-00420-9

A Mixture of Regressions Model of COVID-19 Death Rates and Population Comorbidities

Maleki, M. , McLachlan, G. J. , Gurewitsch, R. , Aruru, M. and Pyne, S. (2020). A Mixture of Regressions Model of COVID-19 Death Rates and Population Comorbidities. Statistics and Applications, 18 (1), 295-306.

Approximation by finite mixtures of continuous density functions that vanish at infinity

Nguyen, T. Tin, Nguyen, Hien D., Chamroukhi, Faicel and McLachlan, Geoffrey J. (2020). Approximation by finite mixtures of continuous density functions that vanish at infinity. Cogent Mathematics and Statistics, 7 (1). doi: 10.1080/25742558.2020.1750861

Mini-batch learning of exponential family finite mixture models

Nguyen, Hien D., Forbes, Florence and McLachlan, Geoffrey J. (2020). Mini-batch learning of exponential family finite mixture models. Statistics and Computing, 30 (4), 731-748. doi: 10.1007/s11222-019-09919-4

A bivariate joint frailty model with mixture framework for survival analysis of recurrent events with dependent censoring and cure fraction

Tawiah, Richard, McLachlan, Geoffrey J. and Ng, Shu Kay (2020). A bivariate joint frailty model with mixture framework for survival analysis of recurrent events with dependent censoring and cure fraction. Biometrics, 76 (3) biom.13202, 753-766. doi: 10.1111/biom.13202

On approximations via convolution-defined mixture models

Nguyen, Hien D. and McLachlan, Geoffrey (2019). On approximations via convolution-defined mixture models. Communications in Statistics - Theory and Methods, 48 (16), 3945-3955. doi: 10.1080/03610926.2018.1487069

False discovery rate control for grouped or discretely supported p-values with application to a neuroimaging study

Nguyen, Hien D., Yee, Yohan, McLachlan, Geoffrey J. and Lerch, Jason P. (2019). False discovery rate control for grouped or discretely supported p-values with application to a neuroimaging study. SORT, 43 (2), 1-22. doi: 10.2436/20.8080.02.87

A multilevel survival model with random covariates and unobservable random effects

Tawiah, Rchard, Yau, Kelvin K. W., McLachlan, Geoffrey J., Chambers, Suzanne and Ng, Shu-Kay (2019). A multilevel survival model with random covariates and unobservable random effects. Statistics in Medicine, 38 (6), 1036-1055. doi: 10.1002/sim.8041

Finite mixture models

McLachlan, Geoffrey J., Lee, Sharon X. and Rathnayake, Suren I. (2019). Finite mixture models. Annual Review of Statistics and Its Application, 6 (1), 355-378. doi: 10.1146/annurev-statistics-031017-100325

Mixture cure models with time-varying and multilevel frailties for recurrent event data

Tawiah, Richard, McLachlan, Geoffrey J. and Ng, Shu Kay (2019). Mixture cure models with time-varying and multilevel frailties for recurrent event data. Statistical Methods in Medical Research, 29 (5), 096228021985937-1385. doi: 10.1177/0962280219859377

Skew-normal Bayesian spatial heterogeneity panel data models

Farzammehr, Mohadeseh Alsadat, Zadkarami, Mohammad Reza, McLachlan, Geoffrey J. and Lee, Sharon X. (2019). Skew-normal Bayesian spatial heterogeneity panel data models. Journal of Applied Statistics, 47 (5), 1-23. doi: 10.1080/02664763.2019.1657812

Skew-normal generalized spatial panel data model

Farzammehr, Mohadeseh Alsadat, Zadkarami, Mohammad Reza and McLachlan, Geoffrey J. (2019). Skew-normal generalized spatial panel data model. Communications in Statistics: Simulation and Computation, 50 (11), 1-29. doi: 10.1080/03610918.2019.1622718

Unsupervised pattern recognition of mixed data structures with numerical and categorical features using a mixture regression modelling framework

Ng, Shu-Kay, Tawiah, Richard and McLachlan, Geoffrey J. (2018). Unsupervised pattern recognition of mixed data structures with numerical and categorical features using a mixture regression modelling framework. Pattern Recognition, 88, 261-271. doi: 10.1016/j.patcog.2018.11.022

Randomized mixture models for probability density approximation and estimation

Nguyen, Hien D., Wang, Dianhui and McLachlan, Geoffrey J. (2018). Randomized mixture models for probability density approximation and estimation. Information Sciences, 467, 135-148. doi: 10.1016/j.ins.2018.07.056

logKDE: log-transformed kernel density estimation

Jones, Andrew T., Nguyen, Hien D. and McLachlan, Geoffrey J. (2018). logKDE: log-transformed kernel density estimation. Journal of Open Source Software, 3 (28) 870, 870. doi: 10.21105/joss.00870

Stream-suitable optimization algorithms for some soft-margin support vector machine variants

Nguyen, Hien D., Jones, Andrew T. and McLachlan, Geoffrey J. (2018). Stream-suitable optimization algorithms for some soft-margin support vector machine variants. Japanese Journal of Statistics and Data Science., 1 (1), 81-108. doi: 10.1007/s42081-018-0001-y

A Block EM Algorithm for Multivariate Skew Normal and Skew t-Mixture Models

Lee, Sharon X., Leemaqz, Kaleb L. and McLachlan, Geoffrey J. (2018). A Block EM Algorithm for Multivariate Skew Normal and Skew t-Mixture Models. IEEE Transactions on Neural Networks and Learning Systems, 29 (99) 8310916, 1-11. doi: 10.1109/TNNLS.2018.2805317

A globally convergent algorithm for a lasso-penalized mixture of linear regression models

Lloyd-Jones, Luke R., Nguyen, Hien D. and McLachlan, Geoffrey J. (2018). A globally convergent algorithm for a lasso-penalized mixture of linear regression models. Computational Statistics and Data Analysis, 119, 19-38. doi: 10.1016/j.csda.2017.09.003

Chunked-and-averaged estimators for vector parameters

Nguyen, Hien D. and McLachlan, Geoffrey J. (2018). Chunked-and-averaged estimators for vector parameters. Statistics and Probability Letters, 137, 336-342. doi: 10.1016/j.spl.2018.02.051

EMMIXcskew: an R package for the fitting of a mixture of canonical fundamental skew t-distributions

Lee, Sharon X. and McLachlan, Geoffrey J. (2018). EMMIXcskew: an R package for the fitting of a mixture of canonical fundamental skew t-distributions. Journal of Statistical Software, 83 (3). doi: 10.18637/jss.v083.i03

Whole-volume clustering of time series data from zebrafish brain calcium images via mixture modeling

Nguyen, Hien D., Ullmann, Jeremy F. P., Mclachlan, Geoffrey J., Voleti, Venkatakaushik, Li, Wenze, Hillman, Elizabeth M. C., Reutens, David C. and Janke, Andrew L. (2017). Whole-volume clustering of time series data from zebrafish brain calcium images via mixture modeling. Statistical Analysis and Data Mining, 11 (1), 5-16. doi: 10.1002/sam.11366

Deep Gaussian mixture models

Viroli, Cinzia and McLachlan, Geoffrey J. (2017). Deep Gaussian mixture models. Statistics and Computing, 29 (1), 1-9. doi: 10.1007/s11222-017-9793-z

Some theoretical results regarding the polygonal distribution

Nguyen, Hien D. and McLachlan, Geoffrey J. (2017). Some theoretical results regarding the polygonal distribution. Communications in Statistics: Theory and Methods, 47 (20), 5083-5095. doi: 10.1080/03610926.2017.1386312

Finite mixture models in biostatistics

Lee, Sharon X., Ng, Shu-Kay and McLachlan, Geoffrey J. (2017). Finite mixture models in biostatistics. Handbook of Statistics, 36, 75-102.

Robust mixtures of factor analysis models using the restricted multivariate skew-t distribution

Lin, Tsung-I, Wang, Wan-Lun, McLachlan, Geoffrey J. and Lee, Sharon X. (2017). Robust mixtures of factor analysis models using the restricted multivariate skew-t distribution. Statistical Modelling, 18 (1), 50-72. doi: 10.1177/1471082X17718119

Maximum pseudolikelihood estimation for model-based clustering of time series data

Nguyen, Hien D., McLachlan, Geoffrey J., Orban, Pierre, Bellec, Pierre and Janke, Andrew L. (2017). Maximum pseudolikelihood estimation for model-based clustering of time series data. Neural Computation, 29 (4), 990-1020. doi: 10.1162/NECO_a_00938

A universal approximation theorem for mixture-of-experts models

Nguyen, Hien D., Lloyd-Jones, Luke R. and McLachlan, Geoffrey J. (2016). A universal approximation theorem for mixture-of-experts models. Neural Computation, 28 (12), 2585-2593. doi: 10.1162/NECO_a_00892

Mixture of time-dependent growth models with an application to blue swimmer crab length-frequency data

Lloyd-Jones, Luke R., Nguyen, Hien D., Mclachlan, Geoffrey J., Sumpton, Wayne and Wang, You-Gan (2016). Mixture of time-dependent growth models with an application to blue swimmer crab length-frequency data. Biometrics, 72 (4), 1255-1265. doi: 10.1111/biom.12531

Partial identification in the statistical matching problem

Ahfock, Daniel, Pyne, Saumyadipta, Lee, Sharon X. and McLachlan, Geoffrey J. (2016). Partial identification in the statistical matching problem. Computational Statistics and Data Analysis, 104, 79-90. doi: 10.1016/j.csda.2016.06.005

Progress on a conjecture regarding the triangular distribution

Nguyen, Hien D. and McLachlan, Geoffrey J. (2016). Progress on a conjecture regarding the triangular distribution. Communications in Statistics: Theory and Methods, 46 (22), 11261-11271. doi: 10.1080/03610926.2016.1263742

Linear mixed models with marginally symmetric nonparametric random effects

Nguyen, Hien D. and McLachlan, Geoffrey J. (2016). Linear mixed models with marginally symmetric nonparametric random effects. Computational Statistics and Data Analysis, 103, 151-169. doi: 10.1016/j.csda.2016.05.005

Spatial clustering of time series via mixture of autoregressions models and Markov random fields

Nguyen, Hien D., McLachlan, Geoffrey J., Ullmann, Jeremy F. P. and Janke, Andrew L. (2016). Spatial clustering of time series via mixture of autoregressions models and Markov random fields. Statistica Neerlandica, 70 (4), 414-439. doi: 10.1111/stan.12093

Maximum likelihood estimation of triangular and polygonal distributions

Nguyen, Hien D. and McLachlan, Geoffrey J. (2016). Maximum likelihood estimation of triangular and polygonal distributions. Computational Statistics and Data Analysis, 102, 23-36. doi: 10.1016/j.csda.2016.04.003

Comment on "On nomenclature for, and the relative merits of, two formulations of skew distributions," by A. Azzalini, R. Browne, M. Genton, and P. McNicholas

McLachlan, Geoffrey J. and Lee, Sharon X. (2016). Comment on "On nomenclature for, and the relative merits of, two formulations of skew distributions," by A. Azzalini, R. Browne, M. Genton, and P. McNicholas. Statistics & Probability Letters, 116, 1-5. doi: 10.1016/j.spl.2016.04.004

A block minorization-maximization algorithm for heteroscedastic regression

Nguyen, Hien D., Lloyd-Jones, Luke R. and McLachlan, Geoffrey J. (2016). A block minorization-maximization algorithm for heteroscedastic regression. IEEE Signal Processing Letters, 23 (8) 7501879, 1131-1135. doi: 10.1109/LSP.2016.2586180

Finite mixtures of canonical fundamental skew t-distributions: The unification of the restricted and unrestricted skew t-mixture models

Lee, Sharon X and McLachlan, Geoffrey J (2016). Finite mixtures of canonical fundamental skew t-distributions: The unification of the restricted and unrestricted skew t-mixture models. Statistics and Computing, 26 (3), 573-589. doi: 10.1007/s11222-015-9545-x

Laplace mixture autoregressive models

Nguyen, Hien D., McLachlan, Geoffrey J., Ullmann, Jeremy F. P. and Janke, Andrew L. (2016). Laplace mixture autoregressive models. Statistics and Probability Letters, 110, 18-24. doi: 10.1016/j.spl.2015.11.006

A benchmark for evaluation of algorithms for identification of cellular correlates of clinical outcomes

Aghaeepour, Nima, Chattopadhyay, Pratip, Chikina, Maria, Dhaene, Tom, Van Gassen, Sofie, Kursa, Miron, Lambrecht, Bart N., Malek, Mehrnoush, McLachlan, G. J., Qian, Yu, Qiu, Peng, Saeys, Yvan, Stanton, Rick, Tong, Dong, Vens, Celine, Walkowiak, Slawomir, Wang, Kui, Finak, Greg, Gottardo, Raphael, Mosmann, Tim, Nolan, Garry P., Scheuermann, Richard H. and Brinkman, Ryan R. (2016). A benchmark for evaluation of algorithms for identification of cellular correlates of clinical outcomes. Cytometry Part A, 89 (1), 16-21. doi: 10.1002/cyto.a.22732

Extending mixtures of factor models using the restricted multivariate skew-normal distribution

Lin, Tsung-I, McLachlan, Geoffrey J. and Lee, Sharon X. (2016). Extending mixtures of factor models using the restricted multivariate skew-normal distribution. Journal of Multivariate Analysis, 143, 398-413. doi: 10.1016/j.jmva.2015.09.025

Laplace mixture of linear experts

Nguyen, Hien D. and McLachlan, Geoffrey J. (2016). Laplace mixture of linear experts. Computational Statistics and Data Analysis, 93, 177-191. doi: 10.1016/j.csda.2014.10.016

Mixtures of spatial spline regressions for clustering and classification

Nguyen, Hien D., McLachlan, Geoffrey J. and Wood, Ian A. (2016). Mixtures of spatial spline regressions for clustering and classification. Computational Statistics and Data Analysis, 93, 76-85. doi: 10.1016/j.csda.2014.01.011

Modeling of inter-sample variation in flow cytometric data with the joint clustering and matching procedure

Lee, Sharon X., McLachlan, Geoffrey J. and Pyne, Saumyadipta (2016). Modeling of inter-sample variation in flow cytometric data with the joint clustering and matching procedure. Cytometry Part A, 89 (1), 30-43. doi: 10.1002/cyto.a.22789

Application of multiple imputation for missing values in three-way three-mode multi-environment trial data

Tian, Ting, McLachlan, Geoffrey J., Dieter, Mark J. and Basford, Kaye E. (2015). Application of multiple imputation for missing values in three-way three-mode multi-environment trial data. PLoS One, 10 (12) e0144370, e0144370.1-e0144370.25. doi: 10.1371/journal.pone.0144370

Special issue on "New trends on model-based clustering and classification"

Ingrassia, Salvatore, McLachlan, Geoffrey J. and Govaert, Gerard (2015). Special issue on "New trends on model-based clustering and classification". Advances in Data Analysis and Classification, 9 (4), 367-369. doi: 10.1007/s11634-015-0224-8

Maximum likelihood estimation of Gaussian mixture models without matrix operations

Nguyen, Hien D. and McLachlan, Geoffrey J. (2015). Maximum likelihood estimation of Gaussian mixture models without matrix operations. Advances in Data Analysis and Classification, 9 (4), 371-394. doi: 10.1007/s11634-015-0209-7

Inference on differences between classes using cluster-specific contrasts of mixed effects

Ng, Shu Kay, McLachlan, Geoffrey J., Wang, Kui, Nagymanyoki, Zoltan, Liu, Shubai and Ng, Shu-Wing (2015). Inference on differences between classes using cluster-specific contrasts of mixed effects. Biostatistics, 16 (1), 98-112. doi: 10.1093/biostatistics/kxu028

Nature and man: the goal of bio-security in the course of rapid and inevitable human development

Pyne, Saumyadipta, Lee, Sharon X. and McLachlan, Geoffrey J. (2015). Nature and man: the goal of bio-security in the course of rapid and inevitable human development. Journal of the Indian Society of Agricultural Statistics, 69 (2), 117-125.

A robust factor analysis model using the restricted skew-t distribution

Lin, Tsung-I, Wu, Pal H., McLachlan, Geoffrey J. and Lee, Sharon X. (2014). A robust factor analysis model using the restricted skew-t distribution. Test, 24 (3), 510-531. doi: 10.1007/s11749-014-0422-2

On the number of components in a Gaussian mixture model

McLachlan, Geoffrey J. and Rathnayake, Suren (2014). On the number of components in a Gaussian mixture model. Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery, 4 (5), 341-355. doi: 10.1002/widm.1135

Joint modeling and registration of cell populations in cohorts of high-dimensional flow cytometric data

Pyne, Saumyadipta, Lee, Sharon X., Wang, Kui, Irish, Jonathan, Tamayo, Pablo, Nazaire, Marc-Danie, Duong, Tarn, Ng, Shu-Kay, Hafler, David, Levy, Ronald, Nolan, Garry P., Mesirov, Jill and McLachlan, Geoffrey J. (2014). Joint modeling and registration of cell populations in cohorts of high-dimensional flow cytometric data. PLoS One, 9 (7) e100334, e100334.1-e100334.11. doi: 10.1371/journal.pone.0100334

False discovery rate control in magnetic resonance imaging studies via Markov random fields

Nguyen, Hien D., McLachlan, Geoffrey J., Cherbuin, Nicolas and Janke, Andrew L. (2014). False discovery rate control in magnetic resonance imaging studies via Markov random fields. IEEE Transactions on Medical Imaging, 33 (8) 6811158, 1735-1748. doi: 10.1109/TMI.2014.2322369

Finite mixtures of multivariate skew t-distributions: Some recent and new results

Lee, Sharon and McLachlan, Geoffrey J. (2014). Finite mixtures of multivariate skew t-distributions: Some recent and new results. Statistics and Computing, 24 (2), 181-202. doi: 10.1007/s11222-012-9362-4

Mixture models for clustering multilevel growth trajectories

Ng S.K. and McLachlan G.J. (2014). Mixture models for clustering multilevel growth trajectories. Computational Statistics and Data Analysis, 71, 43-51. doi: 10.1016/j.csda.2012.12.007

The 2nd special issue on advances in mixture models

Boehning, Dankmar, Hennig, Christian, McLachlan, Geoffrey J. and McNicholas, Paul D. (2014). The 2nd special issue on advances in mixture models. Computational Statistics and Data Analysis, 71, 1-2. doi: 10.1016/j.csda.2013.10.010

EMMIXuskew: An R package for Fitting Mixtures of Multivariate Skew t distributions via the EM algorithm

Lee S.X. and McLachlan G.J. (2013). EMMIXuskew: An R package for Fitting Mixtures of Multivariate Skew t distributions via the EM algorithm. Journal of Statistical Software, 55 (12), 1-22. doi: 10.18637/jss.v055.i12

Model-based clustering and classification with non-normal mixture distributions

Lee, Sharon X. and McLachlan, Geoffrey J. (2013). Model-based clustering and classification with non-normal mixture distributions. Statistical Methods and Applications, 22 (4), 427-454. doi: 10.1007/s10260-013-0237-4

Rejoinder to the discussion of "Model-based clustering and classification with non-normal mixture distributions"

Lee, Sharon X. and McLachlan, Geoffrey J. (2013). Rejoinder to the discussion of "Model-based clustering and classification with non-normal mixture distributions". Statistical Methods and Applications, 22 (4), 473-479. doi: 10.1007/s10260-013-0249-0

On mixtures of skew normal and skew t-distributions

Lee, Sharon X. and McLachlan, Geoffrey J. (2013). On mixtures of skew normal and skew t-distributions. Advances in Data Analysis and Classification, 7 (3), 241-266. doi: 10.1007/s11634-013-0132-8

How to find an appropriate clustering for mixed-type variables with application to socio-economic stratification: written contribution to the discussion on the paper by Hennig and Liao

McLachlan, G. J. (2013). How to find an appropriate clustering for mixed-type variables with application to socio-economic stratification: written contribution to the discussion on the paper by Hennig and Liao. Applied Statistics-Journal of the Royal Statistical Society Series C, 62 (3), 309-369. doi: 10.1111/j.1467-9876.2012.01066.x

Critical assessment of automated flow cytometry analysis techniques

Aghaeepour, Nima, Finak, Greg, Hoos, Holger, Mosmann, Tim R., Brinkman, Ryan, Gottardo, Raphael, Scheuermann, Richard H., The FlowCAP Consortium, McLachlan, Geoffrey J., Wang, Kui and The DREAM Consortium (2013). Critical assessment of automated flow cytometry analysis techniques. Nature Methods, 10 (3), 228-238. doi: 10.1038/nmeth.2365

On the classification of microarray gene-expression data

Basford, Kaye E., McLachlan, Geoffrey J. and Rathnayake, Suren I. (2013). On the classification of microarray gene-expression data. Briefings in Bioinformatics, 14 (4) bbs056, 402-410. doi: 10.1093/bib/bbs056

Clustering of time-course gene expression profiles using normal mixture models with autoregressive random effects

Wang, Kui, Ng, Shu Kay and McLachlan, Geoffrey J. (2012). Clustering of time-course gene expression profiles using normal mixture models with autoregressive random effects. Bmc Bioinformatics, 13 (1) 300, 300.1-300.14. doi: 10.1186/1471-2105-13-300

Discriminant analysis

McLachlan, Geoffrey J. (2012). Discriminant analysis. Wiley Interdisciplinary Reviews: Computational Statistics., 4 (5), 421-431. doi: 10.1002/wics.1219

Conservation and divergence in Toll-like receptor 4-regulated gene expression in primary human versus mouse macrophages

Schroder, Kate, Irvine, Katharine M., Taylor, Martin S., Bokil, Nilesh J., Le Cao, Kim-Anh, Masterman, Kelly-Anne, Labzin, Larisa I., Semple, Colin A., Kapetanovic, Ronan, Fairbairn, Lynsey, Akalin, Altuna, Faulkner, Geoffrey J., Baillie, John Kenneth, Gongora, Milena, Daub, Carsten O., Kawaji, Hideya, McLachlan, Geoffrey J., Goldman, Nick, Grimmond, Sean M., Carninci, Piero, Suzuki, Harukazu, Hayashizaki, Yoshihide, Lenhard, Boris, Hume, David A. and Sweet, Matthew J. (2012). Conservation and divergence in Toll-like receptor 4-regulated gene expression in primary human versus mouse macrophages. Proceedings of the National Academy of Sciences of the USA, 109 (16), E944-E953. doi: 10.1073/pnas.1110156109

Top-10 data mining case studies

Melli, Gabor, Wu, Xindong, Beinat, Paul, Bonchi, Francesco, Cao, Longbing, Duan, Rong, Faloutsos, Christos, Ghani, Rayid, Kitts, Brendan, Goethals, Bart, McLachlan, Geoff, Pei, Jian, Srivastava, Ashok and Zaiane, Osmar (2012). Top-10 data mining case studies. International Journal of Information Technology and Decision Making, 11 (2), 389-400. doi: 10.1142/S021962201240007X

A very fast algorithm for matrix factorization

Nikulin, V, Huang, TH, Ng, SK, Rathnayake, SI and McLachlan, GJ (2011). A very fast algorithm for matrix factorization. Statistics and Probability Letters, 81 (7), 773-782. doi: 10.1016/j.spl.2011.02.001

Mixtures of common t-factor analyzers for clustering high-dimensional microarray data

Baek, Jangsun and McLachlan, Geoffrey J. (2011). Mixtures of common t-factor analyzers for clustering high-dimensional microarray data. Bioinformatics, 27 (9) btr112, 1269-1276. doi: 10.1093/bioinformatics/btr112

Classification of high-dimensional microarray data with a two-step procedure via a Wilcoxon criterion and multilayer perceptron

Nikulin, Vladimir, Huang, Tian-Hsiang and McLachlan, Geoffrey J. (2011). Classification of high-dimensional microarray data with a two-step procedure via a Wilcoxon criterion and multilayer perceptron. International Journal of Computational Intelligence and Applications, 10 (1), 1-14. doi: 10.1142/S1469026811002969

Commentary on Steinley and Brusco (2011): Recommendations and cautions

McLachlan, Geoffrey J. (2011). Commentary on Steinley and Brusco (2011): Recommendations and cautions. Psychological Methods, 16 (1), 80-81. doi: 10.1037/a0021141

Assessing the adequacy of Weibull survival models: a simulated envelope approach

Zhao, Yun, Lee, Andy H., Yau, Kelvin K.W. and McLachlan, Geoffrey J. (2011). Assessing the adequacy of Weibull survival models: a simulated envelope approach. Journal of Applied Statistics, 38 (10), 2089-2097. doi: 10.1080/02664763.2010.545115

Testing for Group Structure in High-Dimensional Data

McLachlan, G. J. and Rathnayake, S. I. (2011). Testing for Group Structure in High-Dimensional Data. Journal of Biopharmaceutical Statistics, 21 (6), 1113-1125. doi: 10.1080/10543406.2011.608342

Mixtures of factor analyzers with common factor loadings: Applications to the clustering and visualization of high-dimensional data

Baek, Jangsun, McLachlan, Geoffrey J. and Flack, Lloyd K. (2010). Mixtures of factor analyzers with common factor loadings: Applications to the clustering and visualization of high-dimensional data. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32 (7) 5184847, 1298-1309. doi: 10.1109/TPAMI.2009.149

Integrative mixture of experts to combine clinical factors and gene markers

Le Cao, Kim-Anh, Meugnier, Emmanuelle and McLachlan, Geoffrey J. (2010). Integrative mixture of experts to combine clinical factors and gene markers. Bioinformatics, 26 (9) btq107, 1192-1198. doi: 10.1093/bioinformatics/btq107

Autoantibody profiling to identify biomarkers of key pathogenic pathways in mucinous ovarian cancer

Tang, Liangdan, Yang, Junzheng, Ng, Shu-Kay, Rodriguez, Noah, Choi, Pui-Wah, Vitonis, Allison, Wang, Kui, McLachlan, Geoffrey J., Caiazzo, Robert J., Liu, Brian C.-S., Welch, Brian C.-S., Cramer, Daniel W., Berkowitz, Ross S. and Ng, Shu-Wing (2010). Autoantibody profiling to identify biomarkers of key pathogenic pathways in mucinous ovarian cancer. European Journal of Cancer, 46 (1), 170-179. doi: 10.1016/j.ejca.2009.10.003

A score test for assessing the cured proportion in the long-term survivor mixture model

Zhao, Yun, Lee, Andy H., Yau, Kelvin K. W., Burke, Valerie and McLachlan, Geoffrey J. (2009). A score test for assessing the cured proportion in the long-term survivor mixture model. Statistics In Medicine, 28 (27), 3454-3466. doi: 10.1002/sim.3696

Automated high-dimensional flow cytometric data analysis

Pyne, S., Hu, X., Wang, K., Rossin, E., Lin, T.-I., Maier, L. M., Baecher-Allan, C., McLachlan, G. J., Tamayo, P., Hafler, D. A., De Jager, P. L. and Mesirow, J. P. (2009). Automated high-dimensional flow cytometric data analysis. Proceedings of the National Academy of Sciences of the United States of America, 106 (21), 8519-8524. doi: 10.1073/pnas.0903028106

Classification of imbalanced marketing data with balanced random sets

Nikulin, Vladimir and McLachlan, Geoffrey J. (2009). Classification of imbalanced marketing data with balanced random sets. Journal of Machine Learning Research, 7, 89-100.

Microarray data analysis for differential expression: a tutorial

Suarez, E., Burguete, A. and McLachlan, G. J. (2009). Microarray data analysis for differential expression: a tutorial. Puerto Rico Health Sciences Journal, 28 (2), 89-104.

Wallace's approach to unsupervised learning: The Snob program

Jorgensen, Murray A. and McLachlan, Geoffrey J. (2008). Wallace's approach to unsupervised learning: The Snob program. The Computer Journal, 51 (5), 571-578. doi: 10.1093/comjnl/bxm121

Bivariate mixture modeling of transferrin saturation and serum ferritin concentration in Asians, African Americans, Hispanics, and whites in the Hemochromatosis and Iron Overload Screening (HEIRS) Study

McLaren, C. E., Gordeuk, V. R., Chen, W. -P., Barton, J. C., Action, R. T., Speechley, M., Castro, O., Adams, P. C., Snively, B. M., Harris, E. L., Reboussin, D. M., McLachlan, G. J. and Bean, R. (2008). Bivariate mixture modeling of transferrin saturation and serum ferritin concentration in Asians, African Americans, Hispanics, and whites in the Hemochromatosis and Iron Overload Screening (HEIRS) Study. Translational Research, 151 (2), 97-109. doi: 10.1016/j.trsl.2007.10.002

Characteristic Traffic Load Effects from a Mixture of Loading Events on Short to Medium Span Bridges

Caprani, C. C., O'Brien, E. J. and McLachlan, G. J. (2008). Characteristic Traffic Load Effects from a Mixture of Loading Events on Short to Medium Span Bridges. Structural Safety, 30 (3), 394-404. doi: 10.1016/j.strusafe.2006.11.006

Comments on: Augmenting the bootstrap to analyze high dimensional genomic data

McLachlan, Geoffrey J., Wang, K. and Ng, S. K. (2008). Comments on: Augmenting the bootstrap to analyze high dimensional genomic data. Test, 17 (1), 43-46. doi: 10.1007/s11749-008-0106-x

Large-scale simultaneous inference with applications to the detection of differential expression with microarray data (with discussion)

McLachlan, Geoff J., Wang, Kent and Ng, Shu Kay (2008). Large-scale simultaneous inference with applications to the detection of differential expression with microarray data (with discussion). Statistica, 68 (1), 1-30. doi: 10.6092/issn.1973-2201/3525

On selection biases with prediction rules formed from gene expression data

Zhu, J. X., McLachlan, G. J., Jones, L. B. T. and Wood, I. A. (2008). On selection biases with prediction rules formed from gene expression data. Journal of Statistical Planning and Inference, 138 (2), 374-386. doi: 10.1016/j.jspi.2007.06.003

Professor Gopal Kanji's retirement as editor of Journal of Applied Statistics

Agrawal, M. C., Caudill, Steven B., Chakraborti, S., Draper, Norman, Dryden, Ian, Gani, Joe, Gilmour, Steven G., Govindarajulu, Z., Hand, David J., Franses, Philip Hans, Kacker, Raghu, Khamis, Harry, Khuri, Andre I., Lewis, Toby, Mardia, Kanti, McLachlan, Geoff, Naik, Dayanand, Prescott, Phil, Kumar, V. S. Sampath, Tomizawa, Sadao and Wynn, Henry (2008). Professor Gopal Kanji's retirement as editor of Journal of Applied Statistics. Journal of Applied Statistics, 35 (1), 1-8. doi: 10.1080/02664760701814495

Top 10 Algorithms in Data Mining

Wu, X., Kumar, V., Quinlan, J. R., Ghosh, J., Yang, Q., Motoda, H., McLachlan, G. J., Ng, A., Liu, B., Yu, P. S., Zhou, Z. H., Steinbach, M., Hand, D. J. and Steinberg, D. (2008). Top 10 Algorithms in Data Mining. Knowledge and Information Systems, 14 (1), 1-37. doi: 10.1007/s10115-007-0114-2

Two-component Poisson Mixture Regression Modelling of Count Data With Bivariate Random Effects

Wang, Kui, Yau, Kelvin K. W., Lee, Andy H. and McLachlan, Geoffrey J. (2007). Two-component Poisson Mixture Regression Modelling of Count Data With Bivariate Random Effects. Mathematical and Computer Modelling, 46 (11-12), 1468-1476. doi: 10.1016/j.mcm.2007.02.003

A Score Test for Overdispersion in Zero-Inflated Poisson Mixed Regression Model

Xiang, L., Lee, A. H., Yau, K. K. W. and McLachlan, G. J. (2007). A Score Test for Overdispersion in Zero-Inflated Poisson Mixed Regression Model. Statistics in Medicine, 26 (7), 1608-1622. doi: 10.1002/sim.2616

A tutorial in genetic epidemiology and some considerations in statistical modeling

Suarez, E., Sariol, C. A., Burguete, A. and McLachlan, G. J. (2007). A tutorial in genetic epidemiology and some considerations in statistical modeling. Puerto Rico Health Sciences Journal, 26 (4), 401-421.

Application of gene shaving and mixture models to cluster microarray gene expression data

Do, K. A., McLachlan, G. J., Bean, R. W. and Wen, S. (2007). Application of gene shaving and mixture models to cluster microarray gene expression data. Cancer Informatics, 5, 25-43. doi: 10.1177/117693510700500002

Extension of Mixture-of-Experts Networks for Binary Classification of Hierarchical Data

Ng, S. K. and McLachlan, G. J. (2007). Extension of Mixture-of-Experts Networks for Binary Classification of Hierarchical Data. Artificial Intelligence in Medicine, 41 (1), 57-67. doi: 10.1016/j.artmed.2007.06.001

Extension of the Mixture of Factor Analyzers Model to Incorporate the Multivariate t-Distribution

McLachlan, G. J., Bean, R. W. and Jones, L. B. T. (2007). Extension of the Mixture of Factor Analyzers Model to Incorporate the Multivariate t-Distribution. Computational Statistics & Data Analysis, 51 (11), 5327-5338. doi: 10.1016/j.csda.2006.09.015

Maternity length of stay modelling by Gamma mixture regression with random effects

Lee, Andy H., Wang, Kui, Yau, Kelvin K. W., McLachlan, Geoffrey J. and Ng, Shu Kay (2007). Maternity length of stay modelling by Gamma mixture regression with random effects. Biometrical Journal, 49 (5), 750-764. doi: 10.1002/bimj.200610371

Multilevel Survival Modelling of Recurrent Urinary Tract Infections

Wang, Kui, Yau, Kelvin K. W., Lee, Andy H. and McLachlan, Geoffrey J. (2007). Multilevel Survival Modelling of Recurrent Urinary Tract Infections. Computer Methods and Programs in Biomedicine, 87 (3), 225-229. doi: 10.1016/j.cmpb.2007.05.013

Resolving the latent structure of schizophrenia endophenotypes using expectation-maximization-based finite mixture modeling

Lenzenweger, M. F., McLachlan, G. J. and Rubin, D. B. (2007). Resolving the latent structure of schizophrenia endophenotypes using expectation-maximization-based finite mixture modeling. Journal of Abnormal Psychology, 116 (1), 16-29. doi: 10.1037/0021-843X.116.1.16

Segmentation and intensity estimation of microarray images using a gamma-t mixture model

Baek, J., Son, Y. S. and McLachlan, G. J. (2007). Segmentation and intensity estimation of microarray images using a gamma-t mixture model. Bioinformatics, 23 (4), 458-465. doi: 10.1093/bioinformatics/btl630

Mixture models for detecting differentially expressed genes in microarrays

Jones, L. B. T., Bean, R., McLachlan, G. J. and Zhu, J. X. (2006). Mixture models for detecting differentially expressed genes in microarrays. International Journal of Neural Systems, 16 (5), 353-362. doi: 10.1142/S0129065706000755

A Score Test for Zero-Inflation in Correlated Count Data

Xiang, Liming, Lee, Andy H., Yau, Kelvin K. W. and McLachlan, Geoffrey J. (2006). A Score Test for Zero-Inflation in Correlated Count Data. Statistics In Medicine, 25 (10), 1660-1671. doi: 10.1002/sim.2308

A Mixture model with random-effects components for clustering correlated gene-expression profiles

Ng, SK, McLachlan, GJ, Wang, K, Jones, LBT and Ng, SW (2006). A Mixture model with random-effects components for clustering correlated gene-expression profiles. Bioinformatics, 22 (14), 1745-1752. doi: 10.1093/bioinformatics/btl165

A simple implementation of a normal mixture approach to differential gene expression in multiclass microarrays

McLachlan, GJ, Bean, RW and Jones, LBT (2006). A simple implementation of a normal mixture approach to differential gene expression in multiclass microarrays. Bioinformatics, 22 (13), 1608-1615. doi: 10.1093/bioinformatics/btl148

An Incremental EM-based Learning Approach for On-Line Prediction of Hospital Resource Utilization

Ng, S. K., McLachlan, G. J. and Lee, A. H. (2006). An Incremental EM-based Learning Approach for On-Line Prediction of Hospital Resource Utilization. Artificial Intelligence In Medicine, 36 (3), 257-267. doi: 10.1016/j.artmed.2005.07.003

Multi-level zero-inflated Poisson regression modelling of correlated count data with excess zeros

Lee, AH, Wang, K, Scott, JA, Yau, KKW and McLachlan, GJ (2006). Multi-level zero-inflated Poisson regression modelling of correlated count data with excess zeros. Statistical Methods In Medical Research, 15 (1), 47-61. doi: 10.1191/0962280206sm429oa

Robust cluster analysis via mixture models

McLachlan, G J, Ng, S K and Bean, R W (2006). Robust cluster analysis via mixture models. Austrian Journal of Statistics, 35 (2 & 3), 157-174.

Selection bias in working wit the top genes in supervised classification of tissue samples

Zhu, X., Ambroise, C and McLachlan, G J (2006). Selection bias in working wit the top genes in supervised classification of tissue samples. Statistical Methodology, 3 (1), 29-41. doi: 10.1016/j.stamet.2005.09.011

Cluster analysis of high-dimensional data: A case study

Bean, R and McLachlan, G (2005). Cluster analysis of high-dimensional data: A case study. Intelligent Data Engineering And Automated Learning Ideal 2005, Proceedings, 3578 (-), 302-310.

Use of the EM algorithm to detect QTL affecting multiple-traits in an across half-sib family analysis

Kerr, R. J., McLachlan, G. J. and Henshall, J. M. (2005). Use of the EM algorithm to detect QTL affecting multiple-traits in an across half-sib family analysis. Genetics Selection Evolution, 37 (1), 83-103. doi: 10.1051/gse:2004037

Using mixture models to detect differentially expressed genes

McLachlan, G. J., Bean, R. W., Jones, L. and Zhu, J. X. (2005). Using mixture models to detect differentially expressed genes. Australian Journal Of Experimental Agriculture, 45 (7-8), 859-866. doi: 10.1071/EA05051

Modelling the Distribution of Ischaemic Stroke-Specific Survival Time Using an EM-based Mixture Approach with Random Effects Adjustment

Ng, S. K., McLachlan, G. J., Yau, K. K. W. and Lee, A. H. (2004). Modelling the Distribution of Ischaemic Stroke-Specific Survival Time Using an EM-based Mixture Approach with Random Effects Adjustment. Statistics In Medicine, 23 (17), 2729-2744. doi: 10.1002/sim.1840

Speeding up the EM algorithm for mixture model-based segmentation of magnetic resonance images

Ng, Shu-Kay and McLachlan, Geoffrey J. (2004). Speeding up the EM algorithm for mixture model-based segmentation of magnetic resonance images. Pattern Recognition, 37 (8), 1573-1589. doi: 10.1016/j.patcog.2004.02.012

Using the EM Algorithm to Train Neural Networks: Misconceptions and a New Algorithm for Multiclass Classification

Ng, S. K. and McLachlan, G. J. (2004). Using the EM Algorithm to Train Neural Networks: Misconceptions and a New Algorithm for Multiclass Classification. Ieee Transactions On Neural Networks, 15 (3), 738-749. doi: 10.1109/TNN.2004.826217

Clustering objects on subsets of attributes - Discussion

Hand, DJ, Glasbey, C, Husmeier, D, Gower, JC, van Houwelingen, HC, Bugrien, JB, Nason, G, Critchley, F, Hoff, PD, McLachlan, GJ and Bean, RW (2004). Clustering objects on subsets of attributes - Discussion. Journal of The Royal Statistical Society Series B-statistical Methodology, 66 (4), 839-849.

Mixture modelling for cluster analysis

McLachlan, G. J. and Chang, S. U. (2004). Mixture modelling for cluster analysis. Statistical Methods In Medical Research, 13 (5), 347-361. doi: 10.1191/0962280204sm372ra

On a resampling approach for tests on the number of clusters with mixture model-based clustering of tissue samples

McLachlan, GJ and Khan, N (2004). On a resampling approach for tests on the number of clusters with mixture model-based clustering of tissue samples. Journal of Multivariate Analysis, 90 (1), 90-105. doi: 10.1016/j.jmva.2004.02.002

Model-based clustering in gene expression microarrays: An application to breast cancer data

Mar, J. C. and McLachlan, G. J. (2003). Model-based clustering in gene expression microarrays: An application to breast cancer data. International Journal of Software Engineering And Knowledge Engineering, 13 (6), 579-592. doi: 10.1142/S0218194003001482

Model-based clustering in gene expression microarrays: an application to breast cancer data

Mar, J.C. and McLachlan, G.J. (2003). Model-based clustering in gene expression microarrays: an application to breast cancer data. International Journal of Software Engineering and Knowledge Engineering, 13 (6), 579-592. doi: 10.1142/S0218194003001482

An EM-based Semi-Parametric Mixture Model Approach to the Regression Analysis of Competing-Risks Data

Ng, S. K. and McLachlan, G. J. (2003). An EM-based Semi-Parametric Mixture Model Approach to the Regression Analysis of Competing-Risks Data. Statistics In Medicine, 22 (7), 1097-1111. doi: 10.1002/sim.1371

On the Choice of the Number of Blocks with the Incremental EM Algorithm for the Fitting of Normal Mixtures

Ng, S. K. and McLachlan, G. J. (2003). On the Choice of the Number of Blocks with the Incremental EM Algorithm for the Fitting of Normal Mixtures. Statistics And Computing, 13 (1), 45-55. doi: 10.1023/A:1021987710829

Modelling High-Dimensional Data by Mixtures of Factor Analyzers

McLachlan, G. J., Peel, D. and Bean, R. W. (2003). Modelling High-Dimensional Data by Mixtures of Factor Analyzers. Computational Statistics & Data Analysis, 41 (3-4), 379-388. doi: 10.1016/S0167-9473(02)00183-4

On some variants of the EM algorithm for the fitting of finite mixture models

Ng, A.S. K. and McLachlan, G. J. (2003). On some variants of the EM algorithm for the fitting of finite mixture models. Austrian Journal of Statistics, 32 (1 & 2), 143-161.

Selection bias in gene extraction on the basis of microarray gene-expression data

Ambroise, Christophe and McLachlan, Geoffrey J. (2002). Selection bias in gene extraction on the basis of microarray gene-expression data. Proceedings of the National Academy of Sciences of the United States of America, 99 (10), 6562-6566. doi: 10.1073/pnas.102102699

A mixture model-based approach to the clustering of microarray expression data

McLachlan, GJ, Bean, RW and Peel, D (2002). A mixture model-based approach to the clustering of microarray expression data. Bioinformatics, 18 (3), 413-422. doi: 10.1093/bioinformatics/18.3.413

Influence of patient age and implantation technique on the probability of re-replacement of the homograft aortic valve

Ng, S. K., O'Brien, M. F., Harrocks, S. N. and McLachlan, G. J. (2002). Influence of patient age and implantation technique on the probability of re-replacement of the homograft aortic valve. Journal of Heart Valve Disease, 11 (2), 217-223.

Maximum Likelihood Estimation of Mixture Densities for Binned and Truncated Multivariate Data

Cadez, I. V., Smyth, P., McLachlan, G. J. and McLaren, C. E. (2002). Maximum Likelihood Estimation of Mixture Densities for Binned and Truncated Multivariate Data. Machine Learning, 47 (1), 7-34. doi: 10.1023/A:1013679611503

Nearest-neighbor variance estimation (NNVE): Robust covariance estimation via nearest-neighbor cleaning - Comment

McLachlan, G. J. and Hamaty, K. L. (2002). Nearest-neighbor variance estimation (NNVE): Robust covariance estimation via nearest-neighbor cleaning - Comment. Journal of The American Statistical Association, 97 (460), 1009-1011. doi: 10.1198/016214502388618807

MRI based diffusion and perfusion predictive model to estimate stroke evolution

Rose, Stephen E., Chalk, Jonathan B., Griffin, Mark P., Janke, Andrew L., Chen, Fang, Mclachlan, Geoffrey J., Peel, David, Zelaya, Fernando O., Markus, Hugh S., Jones, Derek K., Simmons, Andrew, O'Sullivan, Michael, Jarosz, Jo M., Strugnell, Wendy and Doddrell, David M. (2001). MRI based diffusion and perfusion predictive model to estimate stroke evolution. Magnetic Resonance Imaging, 19 (8), 1043-1053. doi: 10.1016/S0730-725X(01)00435-0

Letter to the editor

McLachlan, G. J. (2001). Letter to the editor. Journal of Agricultural, Biological, and Environmental Statistics, 6 (2), 302-304. doi: 10.1198/108571101750524797

Fitting mixtures of Kent distributions to aid in joint set identification

Peel, D, Whiten, WJ and McLachlan, GJ (2001). Fitting mixtures of Kent distributions to aid in joint set identification. Journal of The American Statistical Association, 96 (453), 56-63. doi: 10.1198/016214501750332974

Robust Mixture Modelling Using the t Distribution

Peel, D. and McLachlan, G. J. (2000). Robust Mixture Modelling Using the t Distribution. Statistics and Computing, 10 (4), 339-348. doi: 10.1023/A:1008981510081

Heterogeneity in schizophrenia; mixture modelling of age-at-first-admission, gender and diagnosis

Welham, J., McLachlan, G., Davies, G. and McGrath, J. (2000). Heterogeneity in schizophrenia; mixture modelling of age-at-first-admission, gender and diagnosis. Acta Psychiatrica Scandinavica, 101 (4), 312-317. doi: 10.1034/j.1600-0447.2000.101004312.x

Heterogeneity in schizophrenia; mixture modelling of age-at-first admission, gender and diagnosis

Welham, J., McLachlan, G. J., Davies, G. and McGrath, J. J. (2000). Heterogeneity in schizophrenia; mixture modelling of age-at-first admission, gender and diagnosis. Acta Pyschiatrica Scandinavica, 1, 312-317.

Patient-specific Analysis of Sequential Haematological Data by Multiple Linear Regression and Mixture Distribution Modelling

McLaren, C. E., Kambour, E. L., McLachlan, G. J., Lukaski, H. C., Li, X., Brittenham, G. M. and McLaren, G. D. (2000). Patient-specific Analysis of Sequential Haematological Data by Multiple Linear Regression and Mixture Distribution Modelling. Statistics in Medicine, 19 (1), 83-98. doi: 10.1002/(SICI)1097-0258(20000115)19:1<83::AID-SIM246>3.0.CO;2-A

The EMMIX software for the fitting of mixtures of normal and t-components

McLachlan, G. J., Peel, D., Basford, K. E. and Adams, P. (1999). The EMMIX software for the fitting of mixtures of normal and t-components. Journal of Statistical Software, 4 (2)

Constrained mixture models in competing risks problems

Ng, SK, McLachlan, GJ, McGiffin, DC and OBrien, MF (1999). Constrained mixture models in competing risks problems. Environmetrics, 10 (6), 753-767. doi: 10.1002/(SICI)1099-095X(199911/12)10:6<753::AID-ENV388>3.3.CO;2-B

25 years of applied statistics

McLachlan, G (1998). 25 years of applied statistics. Journal of Applied Statistics, 25 (1), 3-22.

Distribution of transferrin saturation in an Australian population: Relevance to the early diagnosis of hemochromatosis

McLarenCE, McLachlanGJ, HallidayJW, WebbSI, LeggettBA, JazwinskaEC, Crawford, DHG, GordeukVR, McLarenGD and PowellLW (1998). Distribution of transferrin saturation in an Australian population: Relevance to the early diagnosis of hemochromatosis. Gastroenterology, 114 (3), 543-549. doi: 10.1016/S0016-5085(98)70538-4

Heterogeneity in schizophrenia: A mixture model analysis based on age-of-onset, gender and diagnosis

McLachlan, G, Welham, J and McGrath, J (1998). Heterogeneity in schizophrenia: A mixture model analysis based on age-of-onset, gender and diagnosis. Schizophrenia Research, 29 (1-2), 25-25. doi: 10.1016/S0920-9964(97)88353-3

Mathematical classification and clustering.

McLachlan, G (1998). Mathematical classification and clustering.. Psychometrika, 63 (1), 93-95. doi: 10.1007/BF02295440

On modifications to the long-term survival mixture model in the presence of competing risks

Ng, SK and McLachlan, GJ (1998). On modifications to the long-term survival mixture model in the presence of competing risks. Journal of Statistical Computation And Simulation, 61 (1-2), 77-96. doi: 10.1080/00949659808811903

Modelling the distribution of stamp paper thickness via finite normal mixtures: The 1872 Hidalgo stamp issue of Mexico revisited

Basford, K. E., Mclachlan, G. J. and York, M. G. (1997). Modelling the distribution of stamp paper thickness via finite normal mixtures: The 1872 Hidalgo stamp issue of Mexico revisited. Journal of Applied Statistics, 24 (2), 169-179.

An algorithm for fitting mixtures of Gompertz distributions to censored survival data

McLachlan, G. J., Ng, S. K., Adams, P., McGiffin, D. C. and Galbraith, A. J. (1997). An algorithm for fitting mixtures of Gompertz distributions to censored survival data. Journal of Statistical Software, 2 (7), 1-23. doi: 10.18637/jss.v002.i07

Modelling the distribution of stamp paper thickness via finite normal mixtures: The 1872 Hidalgo stamp issue of Mexico revisited

Basford, K. E., McLachlan, G. J. and York, M. G. (1997). Modelling the distribution of stamp paper thickness via finite normal mixtures: The 1872 Hidalgo stamp issue of Mexico revisited. Journal of Applied Statistics, 24 (2), 169-180. doi: 10.1080/02664769723783

On the EM algorithm for overdispersed count data

McLachlan, G. J. (1997). On the EM algorithm for overdispersed count data. Statistical Methods in Medical Research, 6 (1), 76-98. doi: 10.1177/096228029700600106

An analysis of valve re-replacement after aortic valve replacement with biologic devices

McGiffin, DC, Galbraith, AJ, OBrien, MF, McLachlan, GJ, Naftel, DC, Adams, P, Reddy, S and Early, L (1997). An analysis of valve re-replacement after aortic valve replacement with biologic devices. Journal of Thoracic And Cardiovascular Surgery, 113 (2), 311-318. doi: 10.1016/S0022-5223(97)70328-3

High-breakdown linear discriminant analysis

Hawkins, DM and McLachlan, GJ (1997). High-breakdown linear discriminant analysis. Journal of The American Statistical Association, 92 (437), 136-143. doi: 10.2307/2291457

On Bayesian analysis of mixtures with an unknown number of components - Discussion

McLachlan, G (1997). On Bayesian analysis of mixtures with an unknown number of components - Discussion. Journal of The Royal Statistical Society Series B-methodological, 59 (4), 758-792.

Standard errors of fitted component means of normal mixtures

Basford, K. E., Greenway, D. R., McLachlan, G. J. and Peel, D. (1997). Standard errors of fitted component means of normal mixtures. Computational Statistics, 12 (1), 1-17.

The impact of the EM algorithm on medical statistics

McLachlan, G. (1997). The impact of the EM algorithm on medical statistics. Statistical Methods in Medical Research, 6 (1), 1-2. doi: 10.1191/096228097668772579

Maximum likelihood clustering via normal mixture models

McLachlan, GJ, Peel, D and Whiten, WJ (1996). Maximum likelihood clustering via normal mixture models. Signal Processing-Image Communication, 8 (2), 105-111. doi: 10.1016/0923-5965(95)00039-9

Likelihood-based approaches to pattern recognition

McLachlan, G. J. (1996). Likelihood-based approaches to pattern recognition. Far East Journal of Mathematical Sciences, 4 (Pt. 1), 1-29.

An Algorithm for the Likelihood Ratio Test of One Versus 2 Components in a Normal Mixture Model Fitted to Grouped and Truncated Data

McLachlan, GJ, McLaren, CE and Matthews, D (1995). An Algorithm for the Likelihood Ratio Test of One Versus 2 Components in a Normal Mixture Model Fitted to Grouped and Truncated Data. Communications in Statistics-Simulation and Computation, 24 (4), 965-985. doi: 10.1080/03610919508813288

Asymptotic Relative Efficiency of the Linear Discriminant Function Under Partial Nonrandom Classification of the Training Data

McLachlan, GJ and Scot, D (1995). Asymptotic Relative Efficiency of the Linear Discriminant Function Under Partial Nonrandom Classification of the Training Data. Journal of Statistical Computation and Simulation, 52 (4), 415-426. doi: 10.1080/00949659508811689

Relationship of Platelet-Aggregation to Bleeding After Cardiopulmonary Bypass

Ray, MJ, Hawson, Gat, Just, Sje, McLachlan, G and Obrien, M (1994). Relationship of Platelet-Aggregation to Bleeding After Cardiopulmonary Bypass. Annals of Thoracic Surgery, 57 (4), 981-986.

Parametric-Estimation in a Genetic Mixture Model with Application to Nuclear Family Data

Shoukri, MM and McLachlan, GJ (1994). Parametric-Estimation in a Genetic Mixture Model with Application to Nuclear Family Data. Biometrics, 50 (1), 128-139. doi: 10.2307/2533203

Estimation of Mixing Proportions in the Presence of Autoregressively Correlated Training Data - the Case of 2 Univariate Normal-Populations

Lawoko, Cro and McLachlan, GJ (1994). Estimation of Mixing Proportions in the Presence of Autoregressively Correlated Training Data - the Case of 2 Univariate Normal-Populations. Communications in Statistics-Simulation and Computation, 23 (3), 591-613. doi: 10.1080/03610919408813189

Neural Networks and Related Methods for Classification - Discussion

Whittle, P, Kay, J, Hand, DJ, Tarassenko, L, Brown, PJ, Titterington, DM, Taylor, C, Gilks, WR, Critchley, F, Mayne, AJ, Wahba, G, Luttrell, SP, Baczkowski, AJ, Mardia, KV, Breiman, L, Buntine, W, Chatfield, C, Deveaux, RD, Darken, CJ, Ungar, LH, Glendinning, RH, Hastie, T, Tibshirani, R, McLachlan, GJ, Michie, D, Owen, AB, Wolpert, DH and Ripley, BD (1994). Neural Networks and Related Methods for Classification - Discussion. Journal of the Royal Statistical Society Series B-Methodological, 56 (3), 437-456.

On the role of finite mixture models in survival analysis.

McLachlan G.J. and McGiffin D.C. (1994). On the role of finite mixture models in survival analysis.. Statistical methods in medical research, 3 (3), 211-226. doi: 10.1177/096228029400300302

Relationship of platelet aggregation to bleeding after cardiopulmonary bypass

Ray, Michael J., Hawson, Geoffrey A.T., Just, Sarah J.E., McLachlan, Geoffery and O'Brien, Mark (1994). Relationship of platelet aggregation to bleeding after cardiopulmonary bypass. The Annals of Thoracic Surgery, 57 (4), 981-986. doi: 10.1016/0003-4975(94)90218-6

An Analysis of Risk-Factors for Death and Mode-Specific Death After Aortic-Valve Replacement with Allograft, Xenograft, and Mechanical Valves

McGiffin, DC, Obrien, MF, Galbraith, AJ, McLachlan, GJ, Stafford, EG, Gardner, Mah, Pohlner, PG, Early, L and Kear, L (1993). An Analysis of Risk-Factors for Death and Mode-Specific Death After Aortic-Valve Replacement with Allograft, Xenograft, and Mechanical Valves. Journal of Thoracic and Cardiovascular Surgery, 106 (5), 895-911. doi: 10.1016/s0022-5223(19)34046-2

A Connection Between the Logit Model, Normal Discriminant-Analysis, and Multivariate Normal Mixtures - Comment

McLachlan, G (1993). A Connection Between the Logit Model, Normal Discriminant-Analysis, and Multivariate Normal Mixtures - Comment. American Statistician, 47 (1), 88-88.

Aortic-Valve Infection - Risk-Factors for Death and Recurrent Endocarditis After Aortic-Valve Replacement

McGiffin, DC, Galbraith, AJ, McLachlan, GJ, Stower, RE, Wong, ML, Stafford, EG, Gardner, Mah, Pohlner, PG and Obrien, MF (1992). Aortic-Valve Infection - Risk-Factors for Death and Recurrent Endocarditis After Aortic-Valve Replacement. Journal of Thoracic and Cardiovascular Surgery, 104 (2), 511-520. doi: 10.1016/s0022-5223(19)34813-5

Fitting finite mixture models in a regression context

Jones, P. N. and McLachlan, G. J. (1992). Fitting finite mixture models in a regression context. Australian Journal of Statistics, 34 (2), 233-240. doi: 10.1111/j.1467-842X.1992.tb01356.x

Improving the convergence rate of the EM algorithm for a mixture model fitted to grouped truncated data

Jones, Peter N. and McLachlan, Geoffrey J. (1992). Improving the convergence rate of the EM algorithm for a mixture model fitted to grouped truncated data. Journal of Statistical Computation and Simulation, 43 (1-2), 31-44. doi: 10.1080/00949659208811426

Cluster analysis and related techniques in medical research

Mclachlan G.J. (1992). Cluster analysis and related techniques in medical research. Statistical Methods in Medical Research, 1 (1), 27-48. doi: 10.1177/096228029200100103

Fitting Mixture Distributions to Phenylthiocarbamide (ptc) Sensitivity

Jones, PN and McLachlan, GJ (1991). Fitting Mixture Distributions to Phenylthiocarbamide (ptc) Sensitivity. American Journal of Human Genetics, 48 (1), 117-120.

The analysis of time-related events after cardiac surgery

McGiffen, David C. and McLachlan, Geoffrey J. (1991). The analysis of time-related events after cardiac surgery. The AustralAsian Journal of Cardiac and Thoracic Surgery, 1 (1), 11-13. doi: 10.1016/1037-2091(91)90007-Y

Algorithm AS 254: maximum likelihood estimation from grouped and truncated data with finite normal mixture models

Jones, P. N. and McLachlan, G. J. (1990). Algorithm AS 254: maximum likelihood estimation from grouped and truncated data with finite normal mixture models. Applied Statistics - Journal of the Royal Statistical Society Series C, 39 (2), 273-282. doi: 10.2307/2347776

Laplace-normal mixtures fitted to wind shear data

Jones, P. N. and McLachlan, G. J. (1990). Laplace-normal mixtures fitted to wind shear data. Journal of Applied Statistics, 17 (2), 271-276. doi: 10.1080/757582839

Mixture-Models for Partially Unclassified Data - a Case-Study of Renal Venous Renin in Hypertension

McLachlan, GJ and Gordon, RD (1989). Mixture-Models for Partially Unclassified Data - a Case-Study of Renal Venous Renin in Hypertension. Statistics in Medicine, 8 (10), 1291-1300. doi: 10.1002/sim.4780081012

Bias Associated with the Discriminant-Analysis Approach to the Estimation of Mixing Proportions

Lawoko, Cro and McLachlan, GJ (1989). Bias Associated with the Discriminant-Analysis Approach to the Estimation of Mixing Proportions. Pattern Recognition, 22 (6), 763-766. doi: 10.1016/0031-3203(89)90012-5

Modeling Mass-Size Particle Data by Finite Mixtures

Jones, PN and McLachlan, GJ (1989). Modeling Mass-Size Particle Data by Finite Mixtures. Communications in Statistics-Theory and Methods, 18 (7), 2629-2646. doi: 10.1080/03610928908830054

Fitting Mixture-Models to Grouped and Truncated Data Via the Em Algorithm

McLachlan, GJ and Jones, PN (1988). Fitting Mixture-Models to Grouped and Truncated Data Via the Em Algorithm. Biometrics, 44 (2), 571-578. doi: 10.2307/2531869

Further Results On Discrimination with Auto-Correlated Observations

Lawoko, Cro and McLachlan, GJ (1988). Further Results On Discrimination with Auto-Correlated Observations. Pattern Recognition, 21 (1), 69-72. doi: 10.1016/0031-3203(88)90073-8

On the Choice of Starting Values for the Em Algorithm in Fitting Mixture-Models

McLachlan, GJ (1988). On the Choice of Starting Values for the Em Algorithm in Fitting Mixture-Models. Statistician, 37 (4-5), 417-425. doi: 10.2307/2348768

A Note On the Aitkin-Rubin Approach to Hypothesis-Testing in Mixture-Models

Quinn, BG, McLachlan, GJ and Hjort, NL (1987). A Note On the Aitkin-Rubin Approach to Hypothesis-Testing in Mixture-Models. Journal of the Royal Statistical Society Series B-Methodological, 49 (3), 311-314. doi: 10.1111/j.2517-6161.1987.tb01700.x

On Bootstrapping the Likelihood Ratio Test Statistic for the Number of Components in a Normal Mixture

McLachlan, GJ (1987). On Bootstrapping the Likelihood Ratio Test Statistic for the Number of Components in a Normal Mixture. Applied Statistics-Journal of the Royal Statistical Society Series C, 36 (3), 318-324. doi: 10.2307/2347790

Assessing the Performance of An Allocation Rule

McLachlan, GJ (1986). Assessing the Performance of An Allocation Rule. Computers & Mathematics with Applications-Part a, 12 (2), 261-272. doi: 10.1016/0898-1221(86)90079-9

Asymptotic Error Rates of the W-Statistics and Z-Statistics When the Training Observations Are Dependent

Lawoko, Cro and McLachlan, GJ (1986). Asymptotic Error Rates of the W-Statistics and Z-Statistics When the Training Observations Are Dependent. Pattern Recognition, 19 (6), 467-471. doi: 10.1016/0031-3203(86)90045-2

Cluster-Analysis in a Randomized Complete Block Design

Basford, KE and McLachlan, GJ (1985). Cluster-Analysis in a Randomized Complete Block Design. Communications in Statistics-Theory and Methods, 14 (2), 451-463. doi: 10.1080/03610928508828924

Discrimination with Auto-Correlated Observations

Lawoko, Cro and McLachlan, GJ (1985). Discrimination with Auto-Correlated Observations. Pattern Recognition, 18 (2), 145-149. doi: 10.1016/0031-3203(85)90038-X

Estimation of allocation rates in a cluster-analysis context

Basford, K. E. and McLachlan, G. J. (1985). Estimation of allocation rates in a cluster-analysis context. Journal of the American Statistical Association, 80 (390), 286-293. doi: 10.2307/2287884

Likelihood Estimation with Normal Mixture-Models

Basford, KE and McLachlan, GJ (1985). Likelihood Estimation with Normal Mixture-Models. Applied Statistics-Journal of the Royal Statistical Society Series C, 34 (3), 282-289. doi: 10.2307/2347474

The Mixture Method of Clustering Applied to 3-Way Data

Basford, KE and McLachlan, GJ (1985). The Mixture Method of Clustering Applied to 3-Way Data. Journal of Classification, 2 (1), 109-125. doi: 10.1007/BF01908066

Estimation of Mixing Proportions - a Case-Study

Do, K and McLachlan, GJ (1984). Estimation of Mixing Proportions - a Case-Study. Applied Statistics-Journal of the Royal Statistical Society Series C, 33 (2), 134-140. doi: 10.2307/2347437

Some Asymptotic Results On the Effect of Auto-Correlation On the Error Rates of the Sample Linear Discriminant Function

Lawoko, Cro and McLachlan, GJ (1983). Some Asymptotic Results On the Effect of Auto-Correlation On the Error Rates of the Sample Linear Discriminant Function. Pattern Recognition, 16 (1), 119-121. doi: 10.1016/0031-3203(83)90014-6

On the Bias and Variance of Some Proportion Estimators

McLachlan, GJ (1982). On the Bias and Variance of Some Proportion Estimators. Communications in Statistics Part B-Simulation and Computation, 11 (6), 715-726. doi: 10.1080/03610918208812290

On the Likelihood Ratio Test for Compound Distributions for Homogeneity of Mixing Proportions

McLachlan, GJ, Lawoko, Cro and Ganesalingam, S (1982). On the Likelihood Ratio Test for Compound Distributions for Homogeneity of Mixing Proportions. Technometrics, 24 (4), 331-334. doi: 10.2307/1267829

On the likelihood ratio test for compound distributions for homogeneity of mixing proportions

McLachlan, G. J., Lawoko, C. R O and Ganesalingam, S. (1982). On the likelihood ratio test for compound distributions for homogeneity of mixing proportions. Technometrics, 24 (4), 331-334. doi: 10.1080/00401706.1982.10487796

Updating a Discriminant Function On the Basis of Unclassified Data

McLachlan, GJ and Ganesalingam, S (1982). Updating a Discriminant Function On the Basis of Unclassified Data. Communications in Statistics Part B-Simulation and Computation, 11 (6), 753-767. doi: 10.1080/03610918208812293

Mathematics and Statistics for the Bio-Sciences - Eason,g, Coles,cw, Gettinby,g

McLachlan, GJ (1981). Mathematics and Statistics for the Bio-Sciences - Eason,g, Coles,cw, Gettinby,g. Biometrics, 37 (2), 417-417. doi: 10.2307/2530436

Some Efficiency Results for the Estimation of the Mixing Proportion in a Mixture of 2 Normal-Distributions

Ganesalingam, S and McLachlan, GJ (1981). Some Efficiency Results for the Estimation of the Mixing Proportion in a Mixture of 2 Normal-Distributions. Biometrics, 37 (1), 23-33. doi: 10.2307/2530519

A Comparison of the Mixture and Classification Approaches to Cluster-Analysis

Ganesalingam, S and McLachlan, GJ (1980). A Comparison of the Mixture and Classification Approaches to Cluster-Analysis. Communications in Statistics Part A-Theory and Methods, 9 (9), 923-933. doi: 10.1080/03610928008827932

A Note On Bias Correction in Maximum Likelihood Estimation with Logistic Discrimination

McLachlan, GJ (1980). A Note On Bias Correction in Maximum Likelihood Estimation with Logistic Discrimination. Technometrics, 22 (4), 621-627. doi: 10.2307/1268202

Error Rate Estimation On the Basis of Posterior Probabilities

Ganesalingam, S and McLachlan, GJ (1980). Error Rate Estimation On the Basis of Posterior Probabilities. Pattern Recognition, 12 (6), 405-413. doi: 10.1016/0031-3203(80)90016-3

Logicstic regression compared to normal discrimination for non-normal populations

Byth, K. and McLachlan, G. J. (1980). Logicstic regression compared to normal discrimination for non-normal populations. Australian Journal of Statistics, 22 (2), 188-196. doi: 10.1111/j.1467-842X.1980.tb01166.x

On the Relationship Between the F-Test and the Overall Error Rate for Variable Selection in 2-Group Discriminant-Analysis

McLachlan, GJ (1980). On the Relationship Between the F-Test and the Overall Error Rate for Variable Selection in 2-Group Discriminant-Analysis. Biometrics, 36 (3), 501-510. doi: 10.2307/2530218

On the mean square error associated with adaptive generalized ridge regression

McLachlan G.J. (1980). On the mean square error associated with adaptive generalized ridge regression. Biometrical Journal, 22 (2), 125-129. doi: 10.1002/bimj.4710220205

Selection of Variables in Discriminant-Analysis

McLachlan, G (1980). Selection of Variables in Discriminant-Analysis. Biometrics, 36 (3), 554-554.

THE COVARIANCE ANALYSIS OF SOME CENSORED SURVIVAL DATA FROM A LARGE SCALE STUDY OF MELANOMA

McLachlan, G. J. and Holt, J. N. (1980). THE COVARIANCE ANALYSIS OF SOME CENSORED SURVIVAL DATA FROM A LARGE SCALE STUDY OF MELANOMA. Australian Journal of Statistics, 22 (3), 237-249. doi: 10.1111/j.1467-842X.1980.tb01173.x

The Efficiency of Efrons Bootstrap Approach Applied to Error Rate Estimation in Discriminant-Analysis

McLachlan, GJ (1980). The Efficiency of Efrons Bootstrap Approach Applied to Error Rate Estimation in Discriminant-Analysis. Journal of Statistical Computation and Simulation, 11 (3-4), 273-279. doi: 10.1080/00949658008810414

A case study of two clustering methods based on maximum likelihood

Ganesalingam, S. and McLachlan, G. J. (1979). A case study of two clustering methods based on maximum likelihood. Statistica Neerlandica, 33 (2), 81-90. doi: 10.1111/j.1467-9574.1979.tb00665.x

Comparison of the Estimative and Predictive Methods of Estimating Posterior Probabilities

McLachlan, G. J. (1979). Comparison of the Estimative and Predictive Methods of Estimating Posterior Probabilities. Communications in Statistics Part A-Theory and Methods, 8 (9), 919-929. doi: 10.1080/03610927908827807

Expected error rates for logistic regression versus normal discriminant analysis

McLachlan, G. J. and Byth, K. (1979). Expected error rates for logistic regression versus normal discriminant analysis. Biometrical Journal, 21 (1), 47-56. doi: 10.1002/bimj.4710210107

Small sample results for a linear discriminant function estimated from a mixture of normal populations

Ganesalingam, S. and McLachlan, G. J. (1979). Small sample results for a linear discriminant function estimated from a mixture of normal populations. Journal of Statistical Computation and Simulation, 9 (2), 151-158. doi: 10.1080/00949657908810306

Biases Associated with Maximum Likelihood Methods of Estimation of Multivariate Logistic Risk Function

Byth, K and McLachlan, GJ (1978). Biases Associated with Maximum Likelihood Methods of Estimation of Multivariate Logistic Risk Function. Communications in Statistics Part A-Theory and Methods, 7 (9), 877-890. doi: 10.1080/03610927808827679

Efficiency of a Linear Discriminant Function Based On Unclassified Initial Samples

Ganesalingam, S and McLachlan, GJ (1978). Efficiency of a Linear Discriminant Function Based On Unclassified Initial Samples. Biometrika, 65 (3), 658-662. doi: 10.1093/biomet/65.3.658

Small sample results for partial classification with the Studentized statistic W

McLachlan, G. J. (1978). Small sample results for partial classification with the Studentized statistic W. Biometrical Journal, 20 (7-8), 639-644. doi: 10.1002/bimj.197800003

Constrained Sample Discrimination with Studentized Classification Statistic-W

McLachlan, GJ (1977). Constrained Sample Discrimination with Studentized Classification Statistic-W. Communications in Statistics Part A-Theory and Methods, 6 (6), 575-583. doi: 10.1080/03610927708827515

Estimating Linear Discriminant Function From Initial Samples Containing a Small Number of Unclassified Observations

McLachlan, GJ (1977). Estimating Linear Discriminant Function From Initial Samples Containing a Small Number of Unclassified Observations. Journal of the American Statistical Association, 72 (358), 403-406. doi: 10.2307/2286807

Note On Choice of a Weighting Function to Give An Efficient Method for Estimating Probability of Misclassification

McLachlan, GJ (1977). Note On Choice of a Weighting Function to Give An Efficient Method for Estimating Probability of Misclassification. Pattern Recognition, 9 (3), 147-149. doi: 10.1016/0031-3203(77)90012-7

The bias of sample based posterior probabilities

McLachlan, G. J. (1977). The bias of sample based posterior probabilities. Biometrical Journal, 19 (6), 421-426. doi: 10.1002/bimj.4710190604

Bias of Apparent Error Rate in Discriminant-Analysis

McLachlan, GJ (1976). Bias of Apparent Error Rate in Discriminant-Analysis. Biometrika, 63 (2), 239-244. doi: 10.2307/2335615

Criterion for Selecting Variables for Linear Discriminant Function

McLachlan, GJ (1976). Criterion for Selecting Variables for Linear Discriminant Function. Biometrics, 32 (3), 529-534. doi: 10.2307/2529742

Further Results On Effect of Intraclass Correlation Among Training Samples in Discriminant-Analysis

McLachlan, GJ (1976). Further Results On Effect of Intraclass Correlation Among Training Samples in Discriminant-Analysis. Pattern Recognition, 8 (4), 273-275. doi: 10.1016/0031-3203(76)90047-9

Confidence Intervals for Conditional Probability of Misallocation in Discriminant-Analysis

McLachlan, GJ (1975). Confidence Intervals for Conditional Probability of Misallocation in Discriminant-Analysis. Biometrics, 31 (1), 161-167. doi: 10.2307/2529717

Iterative Reclassification Procedure for Constructing An Asymptotically Optimal Rule of Allocation in Discriminant-Analysis

McLachlan, GJ (1975). Iterative Reclassification Procedure for Constructing An Asymptotically Optimal Rule of Allocation in Discriminant-Analysis. Journal of the American Statistical Association, 70 (350), 365-369. doi: 10.2307/2285824

Iterative reclassification procedure for constructing an asymptotically optimal rule of allocation in discriminant analysis

McLachlan, G. J. (1975). Iterative reclassification procedure for constructing an asymptotically optimal rule of allocation in discriminant analysis. Journal of the American Statistical Association, 70 (350), 365-369. doi: 10.1080/01621459.1975.10479874

Some Expected Values for Error Rates of Sample Quadratic Discriminant Function

McLachlan, GJ (1975). Some Expected Values for Error Rates of Sample Quadratic Discriminant Function. Australian Journal of Statistics, 17 (3), 161-165. doi: 10.1111/j.1467-842x.1975.tb00953.x

Asymptotic Distributions of Conditional Error Rate and Risk in Discriminant-Analysis

McLachla.GJ (1974). Asymptotic Distributions of Conditional Error Rate and Risk in Discriminant-Analysis. Biometrika, 61 (1), 131-135. doi: 10.1093/biomet/61.1.131

Asymptotic Unbiased Technique for Estimating Error Rates in Discriminant-Analysis

McLachla.GJ (1974). Asymptotic Unbiased Technique for Estimating Error Rates in Discriminant-Analysis. Biometrics, 30 (2), 239-249. doi: 10.2307/2529646

Estimation of Errors of Misclassification On Criterion of Asymptotic Mean-Square Error

McLachla.GJ (1974). Estimation of Errors of Misclassification On Criterion of Asymptotic Mean-Square Error. Technometrics, 16 (2), 255-260. doi: 10.2307/1267948

Relationship in Terms of Asymptotic Mean-Square Error Between Separate Problems of Estimating Each of 3 Types of Error Rate of Linear Discriminant Function

McLachla.GJ (1974). Relationship in Terms of Asymptotic Mean-Square Error Between Separate Problems of Estimating Each of 3 Types of Error Rate of Linear Discriminant Function. Technometrics, 16 (4), 569-575. doi: 10.2307/1267609

The relationship in terms of asymptotic mean square error between the separate problems of estimating each of the three types of error rate of the linear discriminant function

McLachlan, G. J. (1974). The relationship in terms of asymptotic mean square error between the separate problems of estimating each of the three types of error rate of the linear discriminant function. Technometrics, 16 (4), 569-575. doi: 10.1080/00401706.1974.10489239

The errors of allocation and their estimators in the two-population discrimination problem

McLachlan, Geoffrey J. (1973). The errors of allocation and their estimators in the two-population discrimination problem. Bulletin of the Australian Mathematical Society, 9 (01), 149-150. doi: 10.1017/s000497270004301x

Asymptotic Expansion of Expectation of Estimated Error Rate in Discriminant-Analysis

McLachla.GJ (1973). Asymptotic Expansion of Expectation of Estimated Error Rate in Discriminant-Analysis. Australian Journal of Statistics, 15 (3), 210-214. doi: 10.1111/j.1467-842X.1973.tb00201.x

Asymptotic Expansion for Variance of Errors of Misclassification of Linear Discriminant Function

McLachla.GJ (1972). Asymptotic Expansion for Variance of Errors of Misclassification of Linear Discriminant Function. Australian Journal of Statistics, 14 (1), 68-72. doi: 10.1111/j.1467-842X.1972.tb00339.x

Asymptotic Results for Discriminant Analysis When Initial Samples Are Misclassified

McLachla.GJ (1972). Asymptotic Results for Discriminant Analysis When Initial Samples Are Misclassified. Technometrics, 14 (2), 415-&. doi: 10.2307/1267432

Exploratory data analysis of TCGA skin cutaneous melanoma RNA-seq data

Zhang, Min, Arief, Vivi, McLachlan, Geoffrey, Nguyen, Quan and Basford, Kaye (2022). Exploratory data analysis of TCGA skin cutaneous melanoma RNA-seq data. Australasian Applied Statistics Conference (AASC), Inverloch, VIC Australia, 28 November - 2 December 2022.

AEGC Machine Learning Workshop presentation

Chatterjee, Robindra, Valenta, Richard, McLachlan, Geoffrey and Weatherley, Dion (2021). AEGC Machine Learning Workshop presentation. Australian Exploration Geoscience Conference, Online, 14-17 September 2021.

Extending FaultSeg3D to Minerals Seismic: Part 1 – A synthetic 3D-seismic training-volume generator for preparing data replicating a hardrock terrane to train an automatic-fault-prediction algorithm

Chatterjee, Robindra , Valenta, Richard , McLachlan, Geoffrey and Weatherley, Dion (2021). Extending FaultSeg3D to Minerals Seismic: Part 1 – A synthetic 3D-seismic training-volume generator for preparing data replicating a hardrock terrane to train an automatic-fault-prediction algorithm. Australian Earth Science Convention, Virtual, 9-12 February 2021.

On Mean And/or Variance Mixtures of Normal Distributions

Lee, Sharon X. and McLachlan, Geoffrey J. (2021). On Mean And/or Variance Mixtures of Normal Distributions. 12th Scientific Meeting of the Classification and Data Analysis Group of the Italian Statistical Society (CLADAG 2019), Cassino, Italy, 11–13 September 2019. Cham, Switzerland: Springer. doi: 10.1007/978-3-030-69944-4_13

Modelling asset return using multivariate asymmetric mixture models with applications to estimation of Value-at-Risk

Lee, Sharon X. and McLachlan, Geoffrey J. (2020). Modelling asset return using multivariate asymmetric mixture models with applications to estimation of Value-at-Risk. 20th International Congress on Modelling and Simulation - Adapting to Change: The Multiple Roles of Modelling, MODSIM 2013 , Adelaide, SA, Australia, 1 - 6 December 2013. Modelling and Simulation Society of Australia and New Zealand Inc. (MSSANZ).

PPEM: privacy-preserving EM learning for mixture models

Lee, Sharon X., Leemaqz, Kaleb L. and McLachlan, Geoffrey J. (2019). PPEM: privacy-preserving EM learning for mixture models. 8th International Conference on Applications and Techniques in Information Security, ATIS 2017, Auckland, New Zealand, 6-7 July 2017. Oxford, United Kingdom: John Wiley & Sons. doi: 10.1002/cpe.5208

Flexible modelling via multivariate skew distributions

McLachlan, Geoffrey J. and Lee, Sharon X. (2019). Flexible modelling via multivariate skew distributions. Research School on Statistics and Data Science (RSSDS 2019), Melbourne, VIC, Australia, 24–26 July 2019. Singapore, Singapore: Springer Singapore. doi: 10.1007/978-981-15-1960-4_4

Positive data kernel density estimation via the LogKDE package for R

Jones, Andrew T., Nguyen, Hien D. and McLachlan, Geoffrey J. (2019). Positive data kernel density estimation via the LogKDE package for R. AusDM 2018: 16th Australasian Conference on Data Mining, Bahrurst, NSW, Australia, 28 - 30 November 2018. Singapore, Singapore: Springer Singapore. doi: 10.1007/978-981-13-6661-1_21

Corruption-resistant privacy preserving distributed EM algorithm for model-based clustering

Leemaqz, Kaleb L., Lee, Sharon X. and McLachlan, Geoffrey J. (2017). Corruption-resistant privacy preserving distributed EM algorithm for model-based clustering. 2017 IEEE Trustcom/BigDataSE/ICESS, Sydney, NSW, Australia, 1 - 4 August 2017. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/Trustcom/BigDataSE/ICESS.2017.356

Iteratively-reweighted least-squares fitting of support vector machines: a majorization–minimization algorithm approach

Nguyen, Hien D. and McLachlan, Geoffrey J. (2017). Iteratively-reweighted least-squares fitting of support vector machines: a majorization–minimization algorithm approach. Future Technologies Conference (FTC) 2017, Vancouver, Canada, 29-30 November 2017. Piscataway, NJ United States: IEEE.

On the identification of correlated differential features for supervised classification of high-dimensional data

Ng, Shu Kay and McLachlan, Geoffrey J. (2017). On the identification of correlated differential features for supervised classification of high-dimensional data. 15th Conference of the International Federation of Classification Societies (IFCS), Bologna, Italy, July 5-8, 2015. Cham, Switzerland: Springer. doi: 10.1007/978-3-319-55723-6_4

Privacy distributed three-party learning of Gaussian mixture models

Leemaqz, Kaleb L., Lee, Sharon X. and McLachlan, Geoffrey J. (2017). Privacy distributed three-party learning of Gaussian mixture models. International Conference on Applications and Technologies in Information Security (ATIS), Auckland, New Zealand, 6-7 July 2017. Singapore: Springer Singapore. doi: 10.1007/978-981-10-5421-1_7

A simple parallel EM algorithm for statistical learning via mixture models

Lee, Sharon X., Leemaqz, Kaleb L. and McLachlan, Geoffrey J. (2016). A simple parallel EM algorithm for statistical learning via mixture models. International Conference on Digital Image Computing, Gold Coast, QLD, Australia, 30 November - 2 December,2016. Piscataway, NJ, United States: IEEE (Institute for Electrical and Electronic Engineers). doi: 10.1109/DICTA.2016.7796997

Finding group structures in "Big Data" in healthcare research using mixture models

Ng, Shu-Kay and McLachlan, Geoffrey J. (2016). Finding group structures in "Big Data" in healthcare research using mixture models. IEEE International Conference on Bioinformatics and Biomedicine, Shenzhen, China, 15-18 December 2016. Piscataway, NJ, United States: IEE Computer Society. doi: 10.1109/BIBM.2016.7822692

On mixture modelling with multivariate skew distributions

Lee, Sharon X. and McLachlan, Geoffrey J. (2016). On mixture modelling with multivariate skew distributions. COMPSTAT: International Conference on Computational Statistics, Oviedo, Spain, 23-26 August 2016. The Hague, The Netherlands: The International Statistical Institute/International Association for Statistical Computing.

Robust estimation of mixtures of skew-normal distributions

García-Escudero, L. A., Greselin, F., Mayo-Iscar, A. and McLachlan, G. J. (2016). Robust estimation of mixtures of skew-normal distributions. Scientific Meeting of the Italian Statistical Society, Salerno, Italy, 8-10 November 2016. Fisciano, Italy: Dipartimento di Scienze Economiche e Statistiche, University of Salerno..

Unsupervised component-wise EM learning for finite mixtures of skew t-distributions

Lee, Sharon X. and McLachlan, Geoffrey J. (2016). Unsupervised component-wise EM learning for finite mixtures of skew t-distributions. 12th International Conference, ADMA 2016, Gold Coast, QLD, Australia, 12-15 December 2016. New York, NY, United States: Springer. doi: 10.1007/978-3-319-49586-6_49

Application of multiple imputation to incomplete three-way three-mode multi-environment trial data

Tian, T., McLachlan, G., Dieters, M. and Basford, K. (2014). Application of multiple imputation to incomplete three-way three-mode multi-environment trial data. International Biometric Conference, Florence (Italy), 6-11 July 2014. Florence, Italy: International Biometric Society.

Asymptotic inference for hidden process regression models

Nguyen, Hien D. and McLachlan, Geoffrey J. (2014). Asymptotic inference for hidden process regression models. 2014 IEEE Workshop on Statistical Signal Processing (SSP 2014), Gold Coast, Australia, 29 June - 2 July 2014. Piscataway, NJ, United States: IEEE. doi: 10.1109/SSP.2014.6884624

Making sense of a random world through statistics

McLachlan, Geoff (2014). Making sense of a random world through statistics. AusDM 2014, Brisbane, QLD, Australia, 27-28 November 2014. Darlinghurst, NSW, Australia: Australian Computer Society.

Mixture of regression models with latent variables and sparse coefficient parameters

Ng, Shu-Kay and McLachlan, Geoffrey J. (2014). Mixture of regression models with latent variables and sparse coefficient parameters. COMPSTAT 2014, Geneva Switzerland, 19- 22 August 2014. Hague, Netherlands: The International Statistical Institute/International Association for Statistical Computing.

A common factor-analytic model for classification

Sun, Mingzhu and McLachlan, Geoffrey J (2013). A common factor-analytic model for classification. IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2013, Shanghai China, 18 - 21 December 2013. Piscataway, NJ United States: I E E E. doi: 10.1109/BIBM.2013.6732722

Evaluating methods of estimating missing values for three-way three-mode multi-environment trial data

Tian, Ting, McLachlan, Geoff, Dieters, Mark and Basford, Kaye (2013). Evaluating methods of estimating missing values for three-way three-mode multi-environment trial data. Biometrics by the Canals: The International Biometric Society Australasian Region Conference 2013, Mandura, WA, Australia, 1-5 December, 2013.

Modelling asset return using multivariate asymmetric mixture models with applications to estimation of Value-at-Risk

Lee, Sharon X. and McLachlan, Geoffrey J. (2013). Modelling asset return using multivariate asymmetric mixture models with applications to estimation of Value-at-Risk. International Congress on Modelling and Simulation, Adelaide, SA, Australia, 1/12/2013/6/12/2013. Melbourne, Australia: Modelling and Simulation Society of Australia and New Zealand.

On finite mixtures of skew distributions

McLachlan, Geoffrey J. and Leemaqz, Sharon X. (2013). On finite mixtures of skew distributions. 28th International Workshop on Statistical Modelling, Palermo, Italy, 8-12 July 2013. Amsterdam: Statistical Modelling Society.

Preface

Kim, Sunghoon, Li, Guo-Zheng, Ressom, Habtom, Hughes, Michael, Liu, Baoyan, McLachlan, Geoff, Liebman, Michael, Sun, Hongye and Hu, Xiaohua (2013). Preface. 2013 IEEE International Conference on Bioinformatics and Biomedicine, Shanghai, China, 18-21 December 2013. Minerals, Metals and Materials Society. doi: 10.1109/BIBM.2013.6732445

Spatial false discovery rate control for magnetic resonance imaging studies

Nguyen, Hien D., McLachlan, Geoffrey J., Janke, Andrew L., Cherbuin, Nicolas, Sachdev, Perminder and Anstey, Kaarin J. (2013). Spatial false discovery rate control for magnetic resonance imaging studies. International Conference on Digital Image Computing: Techniques and Applications, DICTA 2013, Hobart, TAS, 26 - 28 November 2013. Piscataway, NJ United States: I E E E. doi: 10.1109/DICTA.2013.6691531

Using cluster analysis to improve gene selection in the formation of discriminant rules for the prediction of disease outcomes

Ng, Shu-Kay and McLachlan, Geoffrey J. (2013). Using cluster analysis to improve gene selection in the formation of discriminant rules for the prediction of disease outcomes. IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2013, Shanghai, China, 18 - 21 December 2013. Piscataway, NJ United States: I E E E. doi: 10.1109/BIBM.2013.6732501

A comparative study of two matrix factorization methods applied to the classification of gene expression rate

Nikulin, Vladimir, Huang, Tian-Hsiang and McLachlan, Geoffrey J. (2010). A comparative study of two matrix factorization methods applied to the classification of gene expression rate. IEEE International Conference on Bioinformatics & Biomedicine, Hong Kong, 18-21 December 2010. Los Alamitos, CA, U.S.A.: IEEE Computer Society. doi: 10.1109/bibm.2010.5706640

Automated high-dimensional flow cytometric data analysis

Pyne, Saumyadipta, Hu, Xinli, Wang, Kui, Rossin, Elizabeth, Lin, Tsung-I, Maier, Lisa, Baecher-Allan, Clare, McLachlan, Geoffrey, Tamayo, Pablo, Hafler, David, De Jager, Philip and Mesirov, Jill (2010). Automated high-dimensional flow cytometric data analysis. 14th Annual International Conference on Research in Computational Molecular Biology, Lisbon, Portugal, 25-28 April 2010. Heidelberg, Germany: Springer. doi: 10.1007/978-3-642-12683-3_41

Clustering of High-Dimensional Data via Finite Mixture Models

McLachlan, Geoff J. and Baek, Jangsun (2010). Clustering of High-Dimensional Data via Finite Mixture Models. 32nd Annual Conference of the German-Classification-Society, Hamburg Germany, Jul 16-18, 2008. BERLIN: SPRINGER-VERLAG BERLIN. doi: 10.1007/978-3-642-01044-6_3

Identifying fibre bundles with regularized k-means clustering applied to grid-based data

Nikulin, Vladimir and McLachlan, Geoffrey J. (2010). Identifying fibre bundles with regularized k-means clustering applied to grid-based data. 2010 International Joint Conference on Neural Networks (IJCNN 2010), Barcelona, Spain, 18-23 July 2010. United States: IEEE Computer Society. doi: 10.1109/IJCNN.2010.5596562

On relations between genes and metagenes obtained via gradient-based matrix factorization

Huang, Tian-Hsiang, Nikulin, Vladimir and McLachlan, Geoffrey J. (2010). On relations between genes and metagenes obtained via gradient-based matrix factorization. 2010 IEEE/ICME International Conference on Complex Medical Engineering, Gold Coast, Australia, 13-15 July 2010. Piscataway, United States: IEEE Computer Society. doi: 10.1109/ICCME.2010.5558880

On the gradient-based algorithm for matrix factorization applied to dimensionality reduction

Nikulin, Vladimir and McLachlan, Geoffrey J. (2010). On the gradient-based algorithm for matrix factorization applied to dimensionality reduction. BIOINFORMATICS 2010: 1st International Conference on Bioinformatics, Valencia, Spain, 20-23 January 2010. Portugal: Institute for Systems and Technologies of Information, Control and Communication.

Penalized principal component analysis of microarray data

Nikulin, Vladimir and McLachlan, Geoffrey J. (2010). Penalized principal component analysis of microarray data. 6th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, CIBB 2009, Genoa, Italy, 15-17 October, 2009. Germany: Springer. doi: 10.1007/978-3-642-14571-1_7

RSCTC 2010 Discovery Challenge: Mining DNA microarray data for medical diagnosis and treatment

Wojnarski, Marcin, Janusz, Andrzej, Nyugen, Hung Son, Bazan, Jan, Luo, ChuanJiang, Chen, Ze, Hu, Feng, Wang, Guoyin, Guan, Lihe, Luo, Huan, Gao, Juan, Shen, Yuanxia, Nikulin, Vladimir, Huang, Tian-Hsiang, McLachlan, Geoffrey J., Bosnjak, Matko and Gamberger, Dragan (2010). RSCTC 2010 Discovery Challenge: Mining DNA microarray data for medical diagnosis and treatment. 7th International Conference on Rough Sets and Current Trends in Computing (RSCTC 2010), Warsaw, Poland, 28-30 June 2010. Heidelberg, Germany: Springer. doi: 10.1007/978-3-642-13529-3_3

Use of mixture models in multiple hypothesis testing with applications in bioinformatics

McLachlan, Geoffrey J. and Wockner, Leesa (2010). Use of mixture models in multiple hypothesis testing with applications in bioinformatics. Classification as a Tool for Research (GfKl 2009), Dresden, Germany, 13-18 March 2009. doi: 10.1007/978-3-642-10745-0-18

Classification of imbalanced marketing data with balanced random sets

Nikulin, Vladimir and McLachlan, Geoffrey J. (2009). Classification of imbalanced marketing data with balanced random sets. AISTATS 2009, Clearwater Beach, FL, United States, 16-18 April 2009. Cambridge, MA, United States: M I T Press.

Ensemble approach for the classification of imbalanced data

Nikulin, Vladimir, McLachlan, Geoffrey J. and Ng, Shu Kay (2009). Ensemble approach for the classification of imbalanced data. AI 2009: Advances in Artificial Intelligence, Melbourne, VIC, Australia, 1-4 December 2009. Berlin, Germany: Springer. doi: 10.1007/978-3-642-10439-8_30

Multivariate skew t mixture models: applications to fluorescence-activated cell sorting data

Wang, Kui, Ng, Shu-Kay and McLachlan, Geoffrey J. (2009). Multivariate skew t mixture models: applications to fluorescence-activated cell sorting data. 2009 Conference of Digital Image Computing: Techniques and Applications, Melbourne, Australia, 1-3 December 2009. Los Alamitos, California: IEEE Computer Society. doi: 10.1109/DICTA.2009.88

On a general method for matrix factorisation applied to supervised classification

Nikulin, Vladimir and McLachlan, Geoffrey J. (2009). On a general method for matrix factorisation applied to supervised classification. 2009 IEEE International Conference on Bioinformatics and Biomedicine Workshops, Washington, D.C., U.S.A., 1-4 November 2009. Piscataway, NJ, United States: IEEE. doi: 10.1109/BIBMW.2009.5332135

Regularised k-means clustering for dimension reduction applied to supervised classification

Nikulin, Vladimir and McLachlan, Geoffrey J. (2009). Regularised k-means clustering for dimension reduction applied to supervised classification. Sixth International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics 2009, Genova, Italy, 15-17 October 2009. Salerno, Italy: DMI Proceedings Series.

Clustering via mixture regression models with random effects

McLachlan, G. J., Ng, S. K. and Wang, K. (2008). Clustering via mixture regression models with random effects. 18th Symposium on Computational Statistics (COMSTAT 2008), Porto, Portugal, 24-29 August 2008. Heidelberg, Germany: Physica-Verlag,. doi: 10.1007/978-3-7908-2084-3_33

Merging algorithm to reduce dimensionality in application to web-mining

Nikulin, V and McLachlan, GJ (2007). Merging algorithm to reduce dimensionality in application to web-mining. 20th Australian Joint Conference on Artificial Intelligence, Gold Coast, Qld, Australia, 2-6 December, 2007. Berlin, Germany: Springer Berlin / Heidelberg. doi: 10.1007/978-3-540-76928-6_88

Resolving the latent structure of schizophrenia endophenotypes using em-based finite mixture modeling

Lenzenweger, M. F., McLachlan, G. and Rubin, D. B. (2007). Resolving the latent structure of schizophrenia endophenotypes using em-based finite mixture modeling. 10th International Congress on Schizophrenia Research, Savannah Ga, 02-06 April 2005. Oxford, United Kingdom: Oxford University Press. doi: 10.1093/schbul/sbm004

Subpopulations with iron deficiency, liver disease, or HFE mutations revealed by statistical mixture modeling of transferrin saturation and serum ferritin concentration in Asians, African American, Hispanics, and Whites

McLaren, C. E., Gordeuk, V. R., Chen, W. P., Barton, J. C., Acton, R. T., Speechley, M., Castro, O., Adams, P. C., Snively, B. M., Harris, E. L., Reboussin, D. M., McLachlan, G. J., Bean, R. and McLaren, G. D. (2007). Subpopulations with iron deficiency, liver disease, or HFE mutations revealed by statistical mixture modeling of transferrin saturation and serum ferritin concentration in Asians, African American, Hispanics, and Whites. 49th Annual Meeting of the American Society of Hematology, Atlanta, GA, U.S.A., 8 - 11 December 2007. Washington, DC, U.S.A.: American Society of Hematology.

A mixture model with random-effects components for clustering correlated gene-expression profiles

Ng, S. K., McLachlan, G. J., Wang, K., Jones, L. Ben-Tovim and Ng, S. W. (2006). A mixture model with random-effects components for clustering correlated gene-expression profiles. doi: 10.1093/bioinformatics/btl165

Clustering replicated microarray data in mixtures of random effects models for varius covariance structures

Ng, S K, McLachlan, G J, Bean, R W and NG, SW (2006). Clustering replicated microarray data in mixtures of random effects models for varius covariance structures. 2006 Workshop on Intelligent Systems for Bioinformatics (WISB, Hobart, Australia, 4 December 2006. Sydney: The Australian Computer Society.

Issues of robustness and high dimensionality in cluster analysis

Basford, Kaye, McLachlan, Geoff and Bean, Richard (2006). Issues of robustness and high dimensionality in cluster analysis. 17th Symposium on Computational Statistics (COMSTAT 2006), Rome, Italy, 28 August - 1 September 2006. Rome, Italy: Physica-Verlag. doi: 10.1007/978-3-7908-1709-6_1

Multilevel modelling for inference of genetic regulatory networks

Ng, Shu-Kay, Wang, Kui and McLachlan, Geoffrey J. (2006). Multilevel modelling for inference of genetic regulatory networks. Complex Systems, Brisbane, Australia, 11-14 December 2005. Bellingham, WA, United States: SPIE - International Society for Optical Engineering. doi: 10.1117/12.638449

Application of mixture models to detect differentially expressed genes

Jones, LBT, Bean, R, McLachlan, G and Zhu, J (2005). Application of mixture models to detect differentially expressed genes. Berlin: Springer-Verlag Berlin. doi: 10.1007/11508069_55

Mixture Model-based Statistical Pattern Recognition of Clustered or Longitudinal Data

Ng, A.S.K. and McLachlan, G. J. (2005). Mixture Model-based Statistical Pattern Recognition of Clustered or Longitudinal Data. WDIC2005, Griffith University, 21 February 2005. Brisbane, Australia: Australian Pattern Recognition Society.

Normalized Gaussian Networks with Mixed Feature Data

Ng, A. S. K. and McLachlan, G. J. (2005). Normalized Gaussian Networks with Mixed Feature Data. 18th Australian Joint Conference on Artificial Intelligence, Sydney, Australia, 5-9 Dec 2005. Berlin, Germany: Springer-Verlag. doi: 10.1007/11589990_101

Linking gene-expression experiments with survival-time data

Jones, L., Ng, A.S. K., Monico, K. A. and McLachlan, G. J. (2004). Linking gene-expression experiments with survival-time data. 19th International Workshop on Statistical Modelling, Florence, 4-8 July 2004. Italy: Firenze University Press.

On the simultaneous use of clinical and microarray expression data in the cluster analysis of tissue samples

McLachlan, G. J., Chang, S., Mar, J. and Ambroise, C. (2004). On the simultaneous use of clinical and microarray expression data in the cluster analysis of tissue samples. Second Asia-Pacific Bioinformatics Conference, Dunedin, New Zealand, 18-22 January 2004. Sydney, Australia: Australian Computer Society.

On clustering by mixture models

McLachlan, GJ, Ng, SK and Peel, D (2003). On clustering by mixture models. 25th Annual Conference of the German-Classification-Society, Munich Germany, Mar 14-16, 2001. BERLIN: SPRINGER-VERLAG BERLIN.

Robust estimation in Gaussian mixtures using multiresolution Kd -trees

Ng, A. S. K. and McLachlan, G. J. (2003). Robust estimation in Gaussian mixtures using multiresolution Kd -trees. Seventh International Conference on Digital Image Computing: Techniques and Applications, DICTA 2003, Sydney, Australia, 10-12 December 2003. Melbourne, Australia: CSIRO Publishing.

Segmentation of brain MR images with bias field correction

Kim, S-G., Ng, A.S. K., McLachlan, G. J. and Wang, D. (2003). Segmentation of brain MR images with bias field correction. WDIC 2003, The University of Queensland, Brisbane, 7 February 2003. Brisbane, Australia: The University of Queensland.

On speeding up the EM algorithm in pattern recognition: A comparison of incremental and multiresolution KD -tree-based approaches

Ng, A.S. K. and McLachlan, G. J. (2002). On speeding up the EM algorithm in pattern recognition: A comparison of incremental and multiresolution KD -tree-based approaches. Proc. of the Sixth Digital Image Computing Techniques & Applications, Melbourne University, 21-22 January. Melbourne: Australian Pattern Recognition Society.

Mixture of factor analyzers

McLachlan, G. J. and Peel, D. (2000). Mixture of factor analyzers. Seventh International Conference on Machine Learning (ICML - 2000), California, United States, June 29 - July 2 2000. San Francisco, CA United States: Morgan Kaufmann.

Multivariate mixture models for classification of anemias

McLaren, C. E., Cadez, I. V., Smyth, P. and McLachlan, G. J. (2000). Multivariate mixture models for classification of anemias. 2000 Proceedings of the Biometrics Sect. of the Amer.Stat.Ass, Indianapolis, USA, August 2000. Virginia, USA: American Statistical Association.

Computing issues for the EM algorithm in mixture models

Mclachlan, G. J. and Peel, D. (1999). Computing issues for the EM algorithm in mixture models. Interface '99, Schaumbury, Illinois, June 1999. Fairfax Station, Virginia: Interface Foundation of North America.

Extending the two-way mixture model program EMMIX to analyse incomplete data

Greenway, D. R., Peel, D., Basford, K. E. and McLachlan, G. J. (1999). Extending the two-way mixture model program EMMIX to analyse incomplete data. Biometrics 99, Univ. of Tas., Hobart, Tas, Aust., 12-16 December 1999. Hobart, Aust.: Int. Biomtric Society, Australasian Region.

Hierarchical models for the screening of iron deficiency anemia

Cadez, I. V., McLaren, C. E., Smyth, P. and Mclachlan, G. J. (1999). Hierarchical models for the screening of iron deficiency anemia. Sixteenth International Conference on Machine Learning (ICML-99), Bled, Slovenia, June 27-30, 1999. Los Gatos, California: Morgan Kaufmann.

MIXFIT: An algorithm for the automatic fitting and testing of normal mixture models

McLachlan, GJ and Peel, D (1998). MIXFIT: An algorithm for the automatic fitting and testing of normal mixture models. 14th International Conference on Pattern Recognition, Brisbane Australia, Aug 16-20, 1998. LOS ALAMITOS: IEEE COMPUTER SOC. doi: 10.1109/icpr.1998.711203

Mining in the presence of selectivity bias and its application to reject inference

Feelders, A. J., Chang, Soong and McLachlan, G. J. (1998). Mining in the presence of selectivity bias and its application to reject inference. 4th International Conference on Knowledge Discovery and Data Mining, New York, United States, 27-31 August 1998. AAAI Press.

Robust cluster analysis via mixtures of multivariate t-distributions

McLachlan G.J. and Peel D. (1998). Robust cluster analysis via mixtures of multivariate t-distributions. 7th Joint IAPR International Workshop on Structural and Syntactic Pattern Recognition, SSPR 1998 and 2nd International Workshop on Statistical Techniques in Pattern Recognition, SPR 1998, August 11, 1998-August 13, 1998. Springer Verlag.

Clustering of magnetic resonance images

McLachlan, GJ, Ng, SK, Galloway, GJ and Wang, D (1996). Clustering of magnetic resonance images. Symposium of the Statistical-Computing-Section, at the Annual Meeting of the American-Statistical-Association, Chicago Il, Aug 04-08, 1996. ALEXANDRIA: AMER STATISTICAL ASSOC.

The distribution of transferrin saturation in an asymptomatic Australian population: relevance to the early diagnosis of hemochromatosis Washington, from. December 1-5, 1995.

McLaren, C.E., McLachlan, G.J., Webb, S.J., Jazwinska, E.C., Crawford, D.H.G., Gordeuk, V.R., McLaren, G.D. and Powell, LW (1995). The distribution of transferrin saturation in an asymptomatic Australian population: relevance to the early diagnosis of hemochromatosis Washington, from. December 1-5, 1995.. 37th Annual Meeting of the American Society of Hematology, Seattle, WA, United States, 1-5 December 1995. Washington, DC, United States: American Society of Hematology.

Early Detection of the Development of Iron-Deficiency by Patient-Specific Sequential-Analysis of Hematological Tests

McLaren, CE, McLaren, GD, Kambour, EL, McLachlan, GJ, Lukaski, HC, Li, X and Brittenham, GM (1994). Early Detection of the Development of Iron-Deficiency by Patient-Specific Sequential-Analysis of Hematological Tests. PHILADELPHIA: SLACK INC.

Early Detection of the Development of Iron-Deficiency by Patient-Specific Sequential-Analysis of Hematological Tests

McLaren, GD, McLaren, CE, Kambour, EL, Lukaski, HC, Xia, L, McLachlan, GJ and Brittenham, GM (1994). Early Detection of the Development of Iron-Deficiency by Patient-Specific Sequential-Analysis of Hematological Tests. THOROFARE: W B SAUNDERS CO.

Allograft aortic valve replacement: Long-term comparative clinical analysis of the viable cryopreserved and antibiotic 4 °C stored valves

O'Brien, M. F., McGiffin, D. C., Stafford, E. G., Gardner, M. A.H., Pohlner, P. F., McLachlan, G. J., Gall, K., Smith, S. and Murphy, E. (1991). Allograft aortic valve replacement: Long-term comparative clinical analysis of the viable cryopreserved and antibiotic 4 °C stored valves. V International Symposium on Cardiac Bioprostheses, Avignon, France, 24–27 May 1991. Hoboken, NJ United States: Wiley-Blackwell. doi: 10.1111/jocs.1991.6.4s.534

Analysis of Some Censored Survival Data From a Large-Scale Study of Melanoma

Holt, JN and McLachlan, GJ (1979). Analysis of Some Censored Survival Data From a Large-Scale Study of Melanoma. WASHINGTON: INTERNATIONAL BIOMETRIC SOC.

Bias Associated with Maximum Likelihood Estimation of Multivariate Logistic Risk Function

McLachlan, GJ (1978). Bias Associated with Maximum Likelihood Estimation of Multivariate Logistic Risk Function. WASHINGTON: INTERNATIONAL BIOMETRIC SOC.

Advances in Data Analysis and Classification

Advances in Data Analysis and Classification. (2015). 9 (4)

Detecting accounting fraud with noisy labels

Ahfock, Daniel, McLachlan, Geoffrey, Yang, Liu and Zhu, Min (2022). Detecting accounting fraud with noisy labels. UQ Business School.

Multivariate analysis: Classification and discriminant analysis

McLachlan, G. J. (2001). Multivariate analysis: Classification and discriminant analysis.