Professor Geoffrey McLachlan's research interests are in: data mining, statistical analysis of microarray, gene expression data, finite mixture models and medical statistics.
Professor McLachlan received his PhD from the University of Queensland in 1974 and his DSc from there in 1994. His current research projects in statistics are in the related fields of classification, cluster and discriminant analyses, image analysis, machine learning, neural networks, and pattern recognition, and in the field of statistical inference. The focus in the latter field has been on the theory and applications of finite mixture models and on estimation via the EM algorithm.
A common theme of his research in these fields has been statistical computation, with particular attention being given to the computational aspects of the statistical methodology. This computational theme extends to Professor McLachlan's more recent interests in the field of data mining.
He is also actively involved in research in the field of medical statistics and, more recently, in the statistical analysis of microarray gene expression data.
Journal Article: 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: . doi:10.1146/annurev-statistics-031017-100325
Journal Article: 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 (2018) A multilevel survival model with random covariates and unobservable random effects. Statistics in Medicine, . doi:10.1002/sim.8041
Journal Article: 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, . doi:10.1016/j.patcog.2018.11.022
Classification methods for providing personalised and class decisions
(2018–2021) ARC Discovery Projects
ARC Training Centre for Innovation in Biomedical Imaging Technology
(2017–2022) ARC Industrial Transformation Training Centres
Expanding the Role of Mixture Models in Statistical Analyses of Big Data
(2017–2020) ARC Discovery Projects
Statistical approaches for automated classification and anomaly detection in Astronomy
Doctor Philosophy
Model-Based Discriminant Analysis of High-Dimensional Data
(2016) Doctor Philosophy
Finite Mixture Models for Regression Problems
(2015) Doctor Philosophy
The EM algorithm and extensions
McLachlan, Geoffrey J. and Krishnan, Thriyambakam The EM algorithm and extensions 2nd ed. Hoboken, NJ, United States: John Wiley & Sons, 2008. doi:10.1002/9780470191613
Analyzing Microarray Gene Expression Data
McLachlan, G. J., Do, K. and Ambroise, C Analyzing Microarray Gene Expression Data. New York: Wiley-Interscience, 2004.
Analyzing microarray gene expression data
McLachlan, Geoffrey J., Do, Kim-Anh and Ambroise, Christophe Analyzing microarray gene expression data. Hoboken, NJ, USA: John Wiley & Sons, 2004. doi:10.1002/047172842x
McLachlan, G. J. and Peel, D. Finite Mixture Models. New York: John Wiley & Sons, 2000.
Finite mixture models: McLachlan/finite mixture models
McLachlan, Geoffrey and Peel, David Finite mixture models: McLachlan/finite mixture models. Hoboken, NJ, USA: John Wiley & Sons, 2000. doi:10.1002/0471721182
The EM algorithm and extensions
McLachlan, Geoffrey J. and Krishnan, Thriyambakam The EM algorithm and extensions. New York, United States: Wiley, 1997.
Discriminant analysis and statistical pattern recognition
McLachlan, Geoffrey J. Discriminant analysis and statistical pattern recognition. Hoboken, NJ, USA: John Wiley & Sons, 1992. doi:10.1002/0471725293
Discriminant analysis and statistical pattern recognition
McLachlan, Geoffrey John Discriminant analysis and statistical pattern recognition. New York , United States: Wiley, 1992.
Mixture models : inference and applications to clustering
McLachlan, Geoffrey J. and Basford, Kaye E. Mixture models : inference and applications to clustering. New York, United States: Marcel Dekker, 1988.
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. In Jamie Alcock and Stephen Satchell (Ed.), Asymmetric dependence in finance: diversification, correlation and portfolio management in market downturns (pp. 152-168) Chichester, West Sussex United Kingdom: John Wiley & Sons. doi:10.1002/9781119288992.ch7
McLachlan, G. J., Bean, R. W. and Ng, S. K. (2017). Clustering. In Jonathan M. Keith (Ed.), Bioinformatics Vol. II: Structure, Function, and Applications 2nd ed. (pp. 345-362) 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. In Handbook of Statistics (pp. 75-102) Amsterdam, AE Netherlands: Elsevier. doi:10.1016/bs.host.2017.08.005
Ng, Shu Kay and McLachlan, Geoffrey J. (2017). On the identification of correlated differential features for supervised classification of high-dimensional data. In Francesco Palumbo, Angela Montanari and Maurizio Vichi (Ed.), Data science, innovative developments in data analysis and clustering (pp. 43-56) Berlin: Springer-Verlag. doi:10.1007/978-3-319-55723-6
Nguyen, Hien D., McLachlan, Geoffrey J. and Hill, Michelle M. (2017). Statistical evaluation of labeled comparative profiling proteomics experiments using permutation test. In Shivakumar Keerthikumar and Suresh Mathivanan (Ed.), Proteome bioinformatics (pp. 109-117) 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. In Saumyadipta Pyne, B. L. S. Prakasa Rao and S. B. Rao (Ed.), Big data analytics: methods and applications (pp. 57-74) 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. In 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. In Christian Hennig, Marina Meila, Fionn Murtagh and Roberto Rocci (Ed.), Handbook of cluster analysis (pp. 145-172) Boca Raton, FL, United States: CRC Press. doi:10.1201/b19706
Computation: Expectation-Maximization Algorithm
McLachlan, Geoffrey J. (2015). Computation: Expectation-Maximization Algorithm. In 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
McLachlan, Geoffrey J. (2015). Mixture Models in Statistics. In 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. In 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. In Andrei Y. Yakovlev, Lev Klebanov and Daniel Gaile (Ed.), Statistical methods for microarray data analysis: methods and protocols (pp. 103-119) 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. In Mohamed Medhat Gaber (Ed.), Journeys to data mining: experiences from 15 renowned researchers (pp. 147-171) Heidelberg, Germany: Springer. doi:10.1007/978-3-642-28047-4_12
Ng, Shu Kay, Krishnan, Thriyambakam and McLachlan, Geoffrey J. (2012). The EM algorithm. In James E. Gentle, Wolfgang Karl Hardle and Yuichi Mori (Ed.), Handbook of Computational Statistics: Concepts and Methods 2nd. rev. and updated ed. ed. (pp. 139-172) Berlin & New York: Springer. doi:10.1007/978-3-642-21551-3__6
Mixtures of factor analysers for the analysis of high-dimensional data
McLachlan, Geoffrey J., Baek, Jangsun and Rathnayake, Suren I. (2011). Mixtures of factor analysers for the analysis of high-dimensional data. In Kerrie L. Mengersen, Christian P. Robert and D. Michael Titterington (Ed.), Mixtures: estimation and applications (pp. 189-212) Chichester, West Sussex, United Kingdom: John Wiley & Sons. doi:10.1002/9781119995678.ch9
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. In Kerrie L. Mengersen, Christian P. Robert and D. Michael Titterington (Ed.), Mixture estimation and applications (pp. 171-191) Chichester, United Kingdom: John Wiley and Sons.
Clustering of high-dimensional and correlated data
McLachlan, Geoffrey J., Ng, Shu-Kay and Wang, K. (2010). Clustering of high-dimensional and correlated data. In Francesco Palumbo, Carlo Natale Lauro and Michael J. Greenacre (Ed.), 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) 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. In Andreas Fink, Berthold Lausen, Wilfried Seidel and Alfred Ultsch (Ed.), 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) Heidelberg, Germany: Springer-Verlag. doi:10.1007/978-3-642-01044-6
Ng, Shu-Kay and McLachlan, Geoffrey J. (2010). Expert networks with mixed continuous and categorical feature variables: A location modeling approach.. In Hannah Peters and Mia Vogel (Ed.), Machine learning research progress (pp. 355-368) 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. In Hermann Locarek-Junge and Claus Weihs (Ed.), 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) 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. In Andriani Daskalaki (Ed.), Handbook of Research on Systems Biology Applications in Medicine (pp. 209-220) United States: IGI Global. doi:10.4018/978-1-60566-076-9.ch011
McLachlan, G. J. and Ng, S-K. (2009). EM. In Wu, X. and Kumar, V. (Ed.), The Top Ten Algorithms in Data Mining (pp. 93-115) Florida, United States: Chapman & Hall/CRC.
McLachlan, G. J. (2009). Model-based clustering. In Steven D. Brown, Roma Tauler and Beata Walczak (Ed.), Comprehensive chemometrics: chemical and biochemical data analysis (pp. 655-681) 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. In Tuan Pham (Ed.), Computational biology: issues and applications in oncology (pp. 55-76) New York, United States: Springer. doi:10.1007/978-1-4419-0811-7_3
McLachlan, G. J., Bean, R. W. and Ng, S.-K. (2008). Clustering. In J. M. Keith (Ed.), Bioinformatics, volume 2: Structure, function and applications (pp. 423-439) 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. In Atanu Biswas, Sujay Datta, Jason P. Fine and Mark R. Segal (Ed.), Statistical advances in the biomedical sciences: clinical trials, epidemiology, survival analysis, and bioinformatics (pp. 365-383) 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. In Balakrishnan, N., Pena, E. A. and Silvapulle, M. J. (Ed.), Beyond Parametrics in Interdisciplinary Research: Festschrift in Honor of Professor Pranab K. Sen (pp. 364-376) United States: Institute of Mathematical Statistics. doi:10.1214/193940307000000284
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. In Jennifer S. Shoemaker and Simon M. Lin (Ed.), Methods of microarray data analysis IV (pp. 163-173) New York, USA: Springer. doi:10.1007/0-387-23077-7_13
Ng, S. K., Krishnan, T. and McLachlan, G. J. (2004). The EM algorithm. In J.E. Gentle, W. Hardle and Y. Mori (Ed.), Handbook of Computational Statistics: Concepts and Methods (pp. 137-168) Germany: Springer-Verlag.
On clustering by mixture models
McLachlan, G. J., Ng, A.S. K. and Peel, D. (2003). On clustering by mixture models. In M. Schwaiger and O. Opitz (Ed.), Exploratory Data Analysis in Empirical Research (pp. 141-148) Germany: Springer.
McLachlan, Geoffrey J., Lee, Sharon X. and Rathnayake, Suren I. (2019) Finite mixture models. Annual Review of Statistics and Its Application, 6 1: . doi:10.1146/annurev-statistics-031017-100325
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 (2018) A multilevel survival model with random covariates and unobservable random effects. Statistics in Medicine, . doi:10.1002/sim.8041
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, . 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
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: 1-11. doi:10.1109/TNNLS.2018.2805317
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
On approximations via convolution-defined mixture models
Nguyen, Hien D. and McLachlan, Geoffrey (2018) On approximations via convolution-defined mixture models. Communications in Statistics - Theory and Methods, . doi:10.1080/03610926.2018.1487069
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
Viroli, Cinzia and McLachlan, Geoffrey J. (2017) Deep Gaussian mixture models. Statistics and Computing, 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
A globally convergent algorithm for a lasso-penalized mixture of linear regression models
Lloyd-Jones, Luke R., Nguyen, Hien D. and McLachlan, Geoffrey J. (2017) 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
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
Nguyen, Hien D., McLachlan, Geoffrey J. and Hill, Michelle M. (2016) Statistical Evaluation of Labeled Comparative Profiling Proteomics Experiments Using Permutation Test. Methods in Molecular Biology, 1549 109-117. doi:10.1007/978-1-4939-6740-7_9
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
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
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: 1131-1135. doi:10.1109/LSP.2016.2586180
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
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
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
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
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
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: e0144370.1-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
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: e100334.1-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: 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
Kim, Sunghoon, Li, Guo-Zheng, Ressom, Habtom, Hughes, Michael, Liu, Baoyan, McLachlan, Geoff, Liebman, Michael, Sun, Hongye and Hu, Xiaohua (2013) Preface. Proceedings - 2013 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2013, . doi:10.1109/BIBM.2013.6732445
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
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
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: 402-410. doi:10.1093/bib/bbs056
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: 300.1-300.14. doi:10.1186/1471-2105-13-300
McLachlan, Geoffrey J. (2012) Discriminant analysis. Wiley Interdisciplinary Reviews: Computational Statistics., 4 5: 421-431. doi:10.1002/wics.1219
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: 1269-1276. doi:10.1093/bioinformatics/btr112
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
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: 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: 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
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
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 1-30. 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.
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
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
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
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. Intelligent Data Engineering And Automated Learning Ideal 2005, Proceedings, 3578 -: 422-431.
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.
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
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
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
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
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
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
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
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
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.
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.
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
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.
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.
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.
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
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
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
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.
McLachlan, G (1993) A Connection Between the Logit Model, Normal Discriminant-Analysis, and Multivariate Normal Mixtures - Comment. American Statistician, 47 1: 88-88.
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.
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
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
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.
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
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
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
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
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
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
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
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
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
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
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
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.
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
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
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
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. In: Proceedings of the 2017 IEEE Trustcom/BigDataSE/ICESS. 2017 IEEE Trustcom/BigDataSE/ICESS, Sydney, NSW, Australia, (1082-1089). 1 - 4 August 2017. doi:10.1109/Trustcom/BigDataSE/ICESS.2017.356
Nguyen, Hien D. and McLachlan, Geoffrey J. (2017). Iteratively-Reweighted Least-Squares Fitting of Support Vector Machines: A Majorization–Minimization Algorithm Approach. In: Proceedings of Future Technologies Conference (FTC) 2017. Future Technologies Conference (FTC) 2017, Vancouver, Canada, (439-446). 29-30 November 2017.
Ng, Shu Kay and McLachlan, Geoffrey J. (2017). On the identification of correlated differential features for supervised classification of high-dimensional data. In: Francesco Palumbo, Angela Montanari and Maurizio Vichi, 15th Conference of the International Federation of Classification Societies (IFCS), Bologna, Italy, (43-57). July 5-8, 2015. 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. In: Lynn Batten, Dong Seong Kim, Xuyun Zhang and Gang Li, Proceedings of the 2017 International Conference on Applications and Technologies in Information Security (ATIS). International Conference on Applications and Technologies in Information Security (ATIS), Auckland, New Zealand, (75-87). 6-7 July 2017. 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. In: Alan Wee-Chung Liew, Brian Lovell, Clinton Fookes, Jun Zhou, Yongsheng Gao, Michael Blumenstein and Zhiyong Wang, 2016 International Conference on Digital Image Computing: Techniques and Applications (DICTA). International Conference on Digital Image Computing, Gold Coast, QLD, Australia, (295-302). 30 November - 2 December,2016. 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. In: Proceedings: 2016 IEEE International Conference on Bioinformatics and Biomedicine. IEEE International Conference on Bioinformatics and Biomedicine, Shenzhen, China, (1214-1219). 15-18 December 2016. 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. In: Ana Colubi, Angela Blanco and Cristian Gatu, Proceedings of COMPSTAT 2016: 22nd International Conference on Computational Statistics. COMPSTAT: International Conference on Computational Statistics, Oviedo, Spain, (137-148). 23-26 August 2016.
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. In: M. Pratesi and C. Perna, 48th Scientific Meeting of the Italian Statistical Society (SIS2016). Scientific Meeting of the Italian Statistical Society, Salerno, Italy, (). 8-10 November 2016.
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. In: Jinyan Li, Xue Li, Shuliang Wang, Jianxin Li and Quan Z. Sheng, Proceedings of the 12th International Conference, ADMA 2016. 12th International Conference, ADMA 2016, Gold Coast, QLD, Australia, (692-699). 12-15 December 2016. 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. In: Abstracts for the XXVIIth International Biometric Conference. International Biometric Conference, Florence (Italy), (). 6-11 July 2014.
Asymptotic inference for hidden process regression models
Nguyen, Hien D. and McLachlan, Geoffrey J. (2014). Asymptotic inference for hidden process regression models. In: 2014 IEEE Workshop on Statistical Signal Processing, SSP 2014. 2014 IEEE Workshop on Statistical Signal Processing (SSP 2014), Gold Coast, Australia, (256-259). 29 June - 2 July 2014. doi:10.1109/SSP.2014.6884624
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. In: M. Gilli, G. Gonzaléz-Rodríguez and A. Nieto-Reyes, Proceedings of COMPSTAT 2014, 21st International Conference on Computational Statistics. COMPSTAT 2014, Geneva Switzerland, (). 19- 22 August 2014.
A common factor-analytic model for classification
Sun, Mingzhu and McLachlan, Geoffrey J (2013). A common factor-analytic model for classification. In: Li, GZ, Kim, S, Hughes, M, McLachlan, G, Sun, H, Hu, X, Ressom, H, Liu, B and Liebman, M, Bioinformatics and Biomedicine (BIBM), 2013 IEEE International Conference on. IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2013, Shanghai China, (19-24). 18 - 21 December 2013. doi:10.1109/BIBM.2013.6732722
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. In: Abstracts: Biometrics by the Canals. Biometrics by the Canals: The International Biometric Society Australasian Region Conference 2013, Mandura, WA, Australia, (72-72). 1-5 December, 2013.
Lee, Sharon X. and McLachlan, Geoffrey J. (2013). Modelling asset return using multivariate asymmetric mixture models with applications to estimation of Value-at-Risk. In: J. Piantadosi, R. S. Anderssen and J. Boland, Proceedings of the 20th International Congress on Modelling and Simulation. International Congress on Modelling and Simulation, Adelaide, SA, Australia, (1128-1234). 1/12/2013/6/12/2013.
On finite mixtures of skew distributions
McLachlan, Geoffrey J. and Leemaqz, Sharon X. (2013). On finite mixtures of skew distributions. In: Vito M.R. Muggeo, Vincenza Capursi, Giovanni Boscaino and Gianfranco Lovison, Proceedings of the 28th International Workshop on Statistical Modelling. 28th International Workshop on Statistical Modelling, Palermo, Italy, (33-44). 8-12 July 2013.
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. In: DeSouza, P, Engelke, U and Rahman, A, Digital Image Computing: Techniques and Applications (DICTA), 2013 International Conference on. International Conference on Digital Image Computing: Techniques and Applications, DICTA 2013, Hobart, TAS, (290-297). 26 - 28 November 2013. doi:10.1109/DICTA.2013.6691531
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. In: Li, GZ, Kim, S, Hughes, M, McLachlan, G, Sun, H, Hu, X, Ressom, H, Liu, B and Liebman, M, Bioinformatics and Biomedicine (BIBM), 2013 IEEE International Conference on. IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2013, Shanghai, China, (267-272). 18 - 21 December 2013. doi:10.1109/BIBM.2013.6732501
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. In: T. Park, L. Chen, L. Wong, S. Tsui, M. Ng and X. Hu, Proceedings of 2010 IEEE International Conference on Bioinformatics and Biomedicine. IEEE International Conference on Bioinformatics & Biomedicine, Hong Kong, (618-621). 18-21 December 2010.
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. In: Bonnie Berger, Research in Computational Molecular Biology: 14th Annual International Conference, RECOMB 2010: Proceedings. 14th Annual International Conference on Research in Computational Molecular Biology, Lisbon, Portugal, (577-577). 25-28 April 2010. 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. In: Fink, A, Lausen, B, Seidel, W and Ultsch, A, Advances in Data Analysis, Data Handling and Business Intelligence - Proc. of the 32nd Annual Conference of the Gesellschaft fur Klassifikation e.V., GfKl 2008 - Joint Conference with BCS and VOC. 32nd Annual Conference of the German-Classification-Society, Hamburg Germany, (33-+). Jul 16-18, 2008. 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. In: V. Piuri, Proceedings of the 2010 International Joint Conference on Neural Networks. 2010 International Joint Conference on Neural Networks (IJCNN 2010), Barcelona, Spain, (2281-2288). 18-23 July 2010. 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. In: Yan Li, Jiajia. Yang, Peng Wen and Jinglong Wu, Proceedings of 2010 IEEE/ICME International Conference on Complex Medical Engineering. 2010 IEEE/ICME International Conference on Complex Medical Engineering, Gold Coast, Australia, (17-22). 13-15 July 2010. 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. In: Ana Fred, Joaquim Filipe and Hugo Gamboa, BIOSTEC 2010: Biomedical Engineering Systems and Technologies. Proceedings of the Third International Joint Conference on Biomedical Engineering Systems and Technologies. BIOINFORMATICS 2010: 1st International Conference on Bioinformatics, Valencia, Spain, (147-152). 20-23 January 2010.
Penalized principal component analysis of microarray data
Nikulin, Vladimir and McLachlan, Geoffrey J. (2010). Penalized principal component analysis of microarray data. In: F. Masulli, L. Peterson and R. Tagliaferri, 6th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics. 6th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, CIBB 2009, Genoa, Italy, (82-96). 15-17 October, 2009. 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. In: Marcin Szczuka, Marzena Kryszkiewicz, Sheela Ramanna, Richard Jensen and Qinghua Hu, Proceedings of the 7th International Conference on Rough Sets and Current Trends in Computing (RSCT 2010). 7th International Conference on Rough Sets and Current Trends in Computing (RSCTC 2010), Warsaw, Poland, (4-19). 28-30 June 2010. doi:10.1007/978-3-642-13529-3_3
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. In: Gideon Dror, Marc Boull´e, Isabelle Guyon, Vincent Lemaire and David Vogel, Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics. AISTATS 2009, Clearwater Beach, FL, United States, (89-100). 16-18 April 2009.
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. In: Ann Nicholson, Xiaodong Li, Randy Goebel, Jörg Siekmann and Wolfgang Wahlster, Lecture Notes in Computer Science. AI 2009: Advances in Artificial Intelligence. 22nd Australasian Joint Conference. Proceedings. AI 2009: Advances in Artificial Intelligence, Melbourne, VIC, Australia, (291-300). 1-4 December 2009. 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. In: Hao Shi, Yanchun Zhang, Murk J. Bottema, Anthony J. Maeder and Brian C. Lovell, Proceedings of Digital Image Computing: Techniques and Applications, 2009. DICTA 2009. 2009 Conference of Digital Image Computing: Techniques and Applications, Melbourne, Australia, (526-531). 1-3 December 2009. 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. In: Jake Chen, Xin Chen, John Ely, Dilek Hakkani-Tr, Jing He, Hui-Huang Hsu, Li Liao, Chunmei Liu, Mihai Pop and Shoba Ranganathan, Proceedings 2009 IEEE International Conference on Bioinformatics and Biomedicine Workshops. 2009 IEEE International Conference on Bioinformatics and Biomedicine Workshops, Washington, D.C., U.S.A., (44-49). 1-4 November 2009. 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. In: Francesco Masulli, Leif Peterson and Roberto Tagliaferri, Proceedings of CIBB 2009, Sixth International Meeting on Computational Intelligence for Bioinformatics and Biostatistics. Sixth International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics 2009, Genova, Italy, (1-10). 15-17 October 2009.
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. In: Paula Brito, COMPSTAT : Proceedings in computational statistics. 18th Symposium on Computational Statistics (COMSTAT 2008), Porto, Portugal, (397-407). 24-29 August 2008. 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. In: Orgun, MA and Thornton, J, AI 2007: Advances in Artificial Intelligence: Proceedings of the 20th Australian Joint Conference on Artificial Intelligence. 20th Australian Joint Conference on Artificial Intelligence, Gold Coast, Qld, Australia, (755-761). 2-6 December, 2007. doi:10.1007/978-3-540-76928-6_88
Lenzenweger, M. F., McLachlan, G. and Rubin, D. B. (2007). Resolving the latent structure of schizophrenia endophenotypes using em-based finite mixture modeling. In: Abstracts of the 11th International Congress on Schizophrenia Research. 10th International Congress on Schizophrenia Research, Savannah Ga, (239-240). 02-06 April 2005. doi:10.1093/schbul/sbm004
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. In: 49th Annual Meeting of the American Society of Hematology, Atlanta, GA, U.S.A., (785A-786A). 8 - 11 December 2007.
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. In: , , (1745-1752). . doi:10.1093/bioinformatics/btl165
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. In: M Boden and T L Bailey, Conferences in Research and Practice in Information Technology. 2006 Workshop on Intelligent Systems for Bioinformatics (WISB, Hobart, Australia, (29-33). 4 December 2006.
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. In: A. Rizzi and M. Vichi, COMPSTAT2006: Proceedings in Computational Statistics. 17th Symposium on Computational Statistics (COMSTAT 2006), Rome, Italy, (3-15). 28 August - 1 September 2006. 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. In: Axel Bender, Complex Systems, Brisbane, Australia, (S390-S390). 11-14 December 2005. doi:10.1117/12.638449
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. In: Brian Lovell and Anthony Maeder, Proceedings of WDIC2005. WDIC2005, Griffith University, (139-144). 21 February 2005.
Normalized Gaussian Networks with Mixed Feature Data
Ng, A. S. K. and McLachlan, G. J. (2005). Normalized Gaussian Networks with Mixed Feature Data. In: S. Zhang and R. Jarvis, AI 2005: Advances in Artificial Intelligence. 18th Australian Joint Conference on Artificial Intelligence, Sydney, Australia, (879-882). 5-9 Dec 2005. 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. In: A. Biggeri and et al, Proceedings of the 19th International Workship on Statistical Modelling. 19th International Workshop on Statistical Modelling, Florence, (71-75). 4-8 July 2004.
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. In: Yi-Ping Phoebe Chen, Proceedings of the Second Asia-Pacific Bioinformatics Conference (APBC2004). Second Asia-Pacific Bioinformatics Conference, Dunedin, New Zealand, (167-171). 18-22 January 2004.
On clustering by mixture models
McLachlan, GJ, Ng, SK and Peel, D (2003). On clustering by mixture models. In: Schwaiger, M and Opitz, O, Exploratory Data Analysis in Empirical Research, Proceedings. 25th Annual Conference of the German-Classification-Society, Munich Germany, (141-148). Mar 14-16, 2001.
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. In: C. Sun, H. Talbot, S. Ourselin and T. Adriaansen, Proceedings of the Seventh International Conference on Digital Image Computing: Techniques and Applications, DICTA 2003. Seventh International Conference on Digital Image Computing: Techniques and Applications, DICTA 2003, Sydney, Australia, (145-154). 10-12 December 2003.
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. In: B.C. Lovell, Proceedings of the 2003 APRS Workshop on Digital Image Computing. WDIC 2003, The University of Queensland, Brisbane, (3-8). 7 February 2003.
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. In: D. Suter and A. Bab-Hadiashar, Digital Image Computing Techniques and Applications. Proc. of the Sixth Digital Image Computing Techniques & Applications, Melbourne University, (116-121). 21-22 January.
McLachlan, G. J. and Peel, D. (2000). Mixture of factor analyzers. In: P. Langley, Proceedings of the Seventeenth International Conference on Machine Learning. Seventh Intern.Conf. on Machine Learning (ICML - 2000), Stanford University, Calfornia, (599-606). June 29 - July 2, 2000.
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. In: 2000 Proceedings of the Biometrics Section of the American Statistical Association. 2000 Proceedings of the Biometrics Sect. of the Amer.Stat.Ass, Indianapolis, USA, (112-117). August 2000.
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. In: K. Berk and M. Pourahmadi, Computing Science and Statistics, Proceedings of the 31st Symposium on the Interface. Interface '99, Schaumbury, Illinois, (421-430). June 1999.
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. In: Biometrics 99, Program and Abstracts. Biometrics 99, Univ. of Tas., Hobart, Tas, Aust., (25-26). 12-16 December 1999.
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. In: I. Bratko and S. Dzeroski, Proceedings of the 1999 International Conference on Machine Learning. Sixteenth International Conference on Machine Learning (ICML-99), Bled, Slovenia, (77-86). June 27-30, 1999.
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. In: Jain, AK, Venkatesh, S and Lovell, BC, Fourteenth International Conference On Pattern Recognition, Vols 1 and 2. 14th International Conference on Pattern Recognition, Brisbane Australia, (553-557). Aug 16-20, 1998.
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. In: Advances in Pattern Recognition - Joint IAPR International Workshops SSPR 1998 and SPR 1998, Proceedings. 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, , (658-666). August 11, 1998-August 13, 1998.
Clustering of magnetic resonance images
McLachlan, GJ, Ng, SK, Galloway, GJ and Wang, D (1996). Clustering of magnetic resonance images. In: American Statistical Association - 1996 Proceedings of the Statistical Computing Section. Symposium of the Statistical-Computing-Section, at the Annual Meeting of the American-Statistical-Association, Chicago Il, (12-17). Aug 04-08, 1996.
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.. In: Abstracts of the 37th Annual Meeting of the American Society of Hematology. 37th Annual Meeting of the American Society of Hematology, Seattle, WA, United States, (502-502). 1-5 December 1995.
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. In: Blood. , , (A116-A116). .
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. In: Clinical Research. , , (A405-A405). .
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. In: Biometrics. , , (697-697). .
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. In: Biometrics. , , (172-172). .
Advances in Data Analysis and Classification
Advances in Data Analysis and Classification (2015) Volume 9 Issue 4
Multivariate analysis: Classification and discriminant analysis
McLachlan, G. J. (2001) Multivariate analysis: Classification and discriminant analysis in N.J. Smelser and P.B. Baltes (eds.) International Encyclopedia of Social and Behavioral Sciences. Oxford: Elsevier Science
Classification methods for providing personalised and class decisions
(2018–2021) ARC Discovery Projects
ARC Training Centre for Innovation in Biomedical Imaging Technology
(2017–2022) ARC Industrial Transformation Training Centres
Expanding the Role of Mixture Models in Statistical Analyses of Big Data
(2017–2020) ARC Discovery Projects
Power Quality Monitoring of Grids with High Penetration of Power Converters
(2017–2020) ARC Linkage Projects
Gene expression profiling in critically ill patients with septic shock: The ADRENAL-GEPS Study
(2015–2018) NHMRC Project Grant
Large-Scale Statistical Inference: Multiple Testing
(2015–2017) ARC Discovery Projects
Advanced Mixture Models for the Analysis of Modern-Day Data
(2014–2017) ARC Discovery Projects
(2014–2016) ARC Linkage Projects
Joint Clustering and Matching of Multivariate Samples Across Objects
(2012–2014) ARC Discovery Projects
Statistical Modelling of Complex, High-Dimensional Data
(2012–2014) Vice-Chancellor's Senior Research Fellowship
A New Approach to Fast Matrix Factorization for the Statistical Analysis of High-Dimensional Data
(2011–2013) ARC Discovery Projects
(2008–2010) ARC Discovery Projects
(2007–2011) ARC Discovery Projects
Noncoding RNAs as prognostic markers and therapeutic targets in breast cancer
(2007–2009) NHMRC Project Grant
ARC Network in Imaging Science and Technology
(2004) ARC Seed Funding for Research Networks
ARC Research Network in Microarray Technology
(2004) ARC Seed Funding for Research Networks
ARC Centre of Excellence in Bioinformatics
(2003–2010) ARC Centres of Excellence
Classification of Microarray Gene-Expression Data
(2003) ARC Discovery Projects
Classification of Microarray Gene-expression Data
(2003) UQ External Support Enabling Grant
Unsupervised learning of finite mixture models in data mining applications
(2003) ARC Discovery Projects
Classification of Multiply Observed Features in Terms of Fitted Densities
(2000–2002) ARC Australian Research Council (Large grants)
On Algorithms for the Automatic Analysis and Segmentation of Correlated Images
(2000–2002) ARC Australian Research Council (Large grants)
Artificial Neural Networks and the EM Algorithm
(1999–2001) ARC Australian Research Council (Large grants)
(1999) ARC Australian Research Council (Small grants)
(1998) ARC Australian Research Council (Small grants)
The Analysis of Plant Adaptation Data with Emphasis on Unbalanced Sets
(1997–1999) ARC Australian Research Council (Large grants)
On mixture models in medical imaging
(1997) ARC Australian Research Council (Small grants)
Approximation of multi-dimensional functions for curve fitting and model building
(1995–1997) ARC Australian Research Council (Large grants)
Statistical approaches for automated classification and anomaly detection in Astronomy
Doctor Philosophy — Principal Advisor
Other advisors:
The Application of Advanced Statistical Methods to Hyperspectral Images in Mineral Exploration
Doctor Philosophy — Associate Advisor
Other advisors:
Maximum pseudolikelihood estimation with Markov random fields in the segmentation of brain magnetic resonance images
Doctor Philosophy — Associate Advisor
Other advisors:
Model-Based Discriminant Analysis of High-Dimensional Data
(2016) Doctor Philosophy — Principal Advisor
Finite Mixture Models for Regression Problems
(2015) Doctor Philosophy — Principal Advisor
Other advisors:
Finite Mixture Modelling using Multivariate Skew Distributions
(2014) Doctor Philosophy — Principal Advisor
Other advisors:
Detection of Differentially Expressed Genes via Mixture Models and Cluster Analysis
(2012) Doctor Philosophy — Principal Advisor
Other advisors:
Statistical analysis of high-dimensional gene expression data
(2009) Doctor Philosophy — Principal Advisor
CLUSTERING WITH MIXED VARIABLES
(2005) Doctor Philosophy — Principal Advisor
Modelling the statistical behaviour of temperature using a modified Brennan and Schwartz 1982 interest rate model
(2004) Master Science — Principal Advisor
Other advisors:
Growth Models and Analysis of Crustacean Growth Data
(2017) Doctor Philosophy — Associate Advisor
(2016) Doctor Philosophy — Associate Advisor
Other advisors:
Genetic Association Studies of Complex Traits
(2013) Doctor Philosophy — Associate Advisor
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TOPOLOGICAL MODELS OF TRANSMEMBRANE PROTEINS FOR SUBCELLULAR LOCALIZATION PREDICTION
() Doctor Philosophy — Associate Advisor
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