G. McLachlan

Andrew L. Janke, Geoffrey J. McLachlan, Nicolas Cherbuin, 2013, 2013 International Conference on Digital Image Computing: Techniques and Applications (DICTA).

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

Geoffrey J. McLachlan, Shu Kay Angus Ng, G. McLachlan, 2005 .

Mini-batch learning of exponential family finite mixture models

F Forbes, G J McLachlan, H D Nguyen, 2019, Statistics and Computing.

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

Sharon X. Lee, G. McLachlan, Tsung-I Lin, 2018 .

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

Andrew L. Janke, Geoffrey J. McLachlan, David C. Reutens, 2016, Stat. Anal. Data Min..

Analyzing Microarray Gene Expression Data

Geoffrey J. McLachlan, Kim-Anh Do, Christophe Ambroise, 2004 .

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

Geoffrey J McLachlan, Christophe Ambroise, G. McLachlan, 2002, Proceedings of the National Academy of Sciences of the United States of America.

Laplace-normal mixtures fitted to wind shear data

Geoffrey J. McLachlan, G. McLachlan, P. N. Jones, 1990 .

Data fusion using factor analysis and low-rank matrix completion

Geoffrey J. McLachlan, Daniel Ahfock, Saumyadipta Pyne, 2021, Statistics and Computing.

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

Geoffrey J. McLachlan, Sharon X. Lee, Tsung I. Lin, 2013, J. Multivar. Anal..

Using the EM algorithm to train neural networks: misconceptions and a new algorithm for multiclass classification

Geoffrey J. McLachlan, Shu-Kay Ng, G. McLachlan, 2004, IEEE Transactions on Neural Networks.

Maximum likelihood clustering via normal mixture models

Geoffrey J. McLachlan, David Peel, W. J. Whiten, 1996, Signal Process. Image Commun..

Parametric estimation in a genetic mixture model with application to nuclear family data.

M M Shoukri, G J McLachlan, G. McLachlan, 1994, Biometrics.

On the choice of starting values for the EM algorithm in fitting mixture models

G. McLachlan, 1988 .

The EM Algorithm

Geoffrey J. McLachlan, Shu-Kay Ng, Thriyambakam Krishnan, 2012 .

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

Geoffrey J. McLachlan, Sharon X. Lee, Sharon X. Lee, 2014, Stat. Comput..

On mixtures of skew normal and skew $$t$$-distributions

Geoffrey J. McLachlan, Sharon X. Lee, Sharon X. Lee, 2012, Adv. Data Anal. Classif..

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

G. McLachlan, Daniel Ahfock, Saumyadipta Pyne, 2021, Computational Statistics & Data Analysis.

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

Geoffrey J. McLachlan, Hien Duy Nguyen, Luke R. Lloyd-Jones, 2016, Comput. Stat. Data Anal..

Statistical Analysis on Microarray Data: Selection of Gene Prognosis Signatures

Geoffrey J. McLachlan, Kim-Anh Lê Cao, G. McLachlan, 2009 .

Top 10 algorithms in data mining

Philip S. Yu, Xindong Wu, Qiang Yang, 2007, Knowledge and Information Systems.

Model-Based Clustering in Gene Expression Microarrays: An Application to Breast Cancer Data

Geoffrey J. McLachlan, J. C. Mar, G. McLachlan, 2003, APBC.

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

Geoffrey J. McLachlan, David Peel, Richard Bean, 2002, Bioinform..

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

Kui Wang, Geoffrey J. McLachlan, Shu-Kay Ng, 2006, Bioinform..

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

N. Goldman, G. McLachlan, Piero Carninci, 2012, Proceedings of the National Academy of Sciences.

Model-based Clustering

G. McLachlan, 2009 .

On the classification of microarray gene-expression data

Geoffrey J. McLachlan, Kaye E. Basford, Suren I. Rathnayake, 2013, Briefings Bioinform..

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

Geoffrey J. McLachlan, Vladimir Nikulin, G. McLachlan, 2009, CIBB 2009.

Clustering of High-Dimensional Data via Finite Mixture Models

Geoffrey J. McLachlan, Jangsun Baek, G. McLachlan, 2008, GfKl.

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

Jason P. Lerch, Hien D. Nguyen, Geoffrey J. McLachlan, 2019 .

Classification of High-Dimensional microarray Data with a Two-Step Procedure via a Wilcoxon Criterion and Multilayer Perceptron

Geoffrey J. McLachlan, Vladimir Nikulin, Tian-Hsiang Huang, 2011, Int. J. Comput. Intell. Appl..

A Very Fast Algorithm for Matrix Factorization pdf

Geoffrey J. McLachlan, Vladimir Nikulin, Shu-Kay Ng, 2010, ArXiv.

Penalized Principal Component Analysis of Microarray Data

Vladimir Nikulin, Geoffrey J. McLachlan, G. McLachlan, 2009, CIBB.

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

G. McLachlan, K. Wang, S. Ng, 2008 .

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

Kui Wang, Geoffrey J. McLachlan, Shu-Kay Ng, 2012, BMC Bioinformatics.

Comprehensive chemometrics: chemical and biochemical data analysis

G. McLachlan, S. Rathnayake, S. Lee, 2020 .

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

Peter Adams, Geoffrey J. McLachlan, David Peel, 1999 .

The EMMIX Algorithm for the Fitting of Normal and t-Components

David Peel, Geoffrey J. McLachlan, G. McLachlan, 1999 .

Modelling high-dimensional data by mixtures of factor analyzers

Geoffrey J. McLachlan, David Peel, Richard Bean, 2003, Comput. Stat. Data Anal..

On approximations via convolution-defined mixture models

Hien D. Nguyen, Geoffrey McLachlan, G. McLachlan, 2016, Communications in Statistics - Theory and Methods.

Mixture Distributions – Further Developments

Geoffrey J. McLachlan, G. McLachlan, 2016 .

An Apparent Paradox: A Classifier Trained from a Partially Classified Sample May Have Smaller Expected Error Rate Than That If the Sample Were Completely Classified.

Geoffrey J. McLachlan, Daniel Ahfock, G. McLachlan, 2019, 1910.09189.

Robust mixture modelling using the t distribution

Geoffrey J. McLachlan, David Peel, G. McLachlan, 2000, Stat. Comput..

The mixture method of clustering applied to three-way data

Geoffrey J. McLachlan, Kaye E. Basford, G. McLachlan, 1985 .

Mixtures of spatial spline regressions for clustering and classification

Geoffrey J. McLachlan, Ian A. Wood, Hien Duy Nguyen, 2016, Comput. Stat. Data Anal..

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

Geoffrey J. McLachlan, G. McLachlan, P. N. Jones, 1992 .

Constrained mixture models in competing risks problems

G. McLachlan, S. Ng, D. McGiffin, 1999 .

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

G. McLachlan, R. Berkowitz, Kui Wang, 2010, European Journal of Cancer.

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

G. McLachlan, R. Tawiah, S. Ng, 2021, Biostatistics.

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

Geoffrey J. McLachlan, Andy H. Lee, Yun Zhao, 2011 .

On the number of components in a Gaussian mixture model

Geoffrey J. McLachlan, Suren I. Rathnayake, G. McLachlan, 2014, Wiley Interdiscip. Rev. Data Min. Knowl. Discov..

Mixture modelling for cluster analysis

G J McLachlan, S U Chang, G. McLachlan, 2004, Statistical methods in medical research.

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

Geoffrey J. McLachlan, Shu-Kay Ng, G. McLachlan, 2004, Pattern Recognit..

Robust Cluster Analysis via Mixtures of Multivariate t-Distributions

Geoffrey J. McLachlan, David Peel, G. McLachlan, 1998, SSPR/SPR.

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

G. McLachlan, H. Nguyen, TrungTin Nguyen, 2020, Journal of Statistical Distributions and Applications.

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

Geoffrey J. McLachlan, Shu Kay Angus Ng, G. McLachlan, 2010 .

Extension of mixture-of-experts networks for binary classification of hierarchical data

Geoffrey J. McLachlan, Shu-Kay Ng, G. McLachlan, 2007, Artif. Intell. Medicine.

An incremental EM-based learning approach for on-line prediction of hospital resource utilization

Geoffrey J. McLachlan, Andy H. Lee, Shu-Kay Ng, 2006, Artif. Intell. Medicine.

Linear mixed models with marginally symmetric nonparametric random effects

Geoffrey J. McLachlan, Hien Duy Nguyen, G. McLachlan, 2016, Comput. Stat. Data Anal..

Two-component Poisson mixture regression modelling of count data with bivariate random effects

Kui Wang, Geoffrey J. McLachlan, Andy H. Lee, 2007, Math. Comput. Model..

Maximum Pseudolikelihood Estimation for Model-Based Clustering of Time Series Data

Andrew L. Janke, Geoffrey J. McLachlan, Pierre Orban, 2016, Neural Computation.

Corruption-Resistant Privacy Preserving Distributed EM Algorithm for Model-Based Clustering

Geoffrey J. McLachlan, Sharon X. Lee, Kaleb L. Leemaqz, 2017, 2017 IEEE Trustcom/BigDataSE/ICESS.

Finite mixtures of canonical fundamental skew $$t$$t-distributions

Geoffrey J. McLachlan, Sharon X. Lee, Sharon X. Lee, 2014, Stat. Comput..

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

Geoffrey J. McLachlan, Jangsun Baek, G. McLachlan, 2011, Bioinform..

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

Geoffrey J. McLachlan, Richard Bean, Liat Ben-Tovim Jones, 2006, Bioinform..

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

Geoffrey J. McLachlan, Edwin J.Y. Koh, Eiman Amini, 2021, Minerals Engineering.

Multiple linear regression & finite mixture distribution modelling for sequential analysis of hematological data

G. McLachlan, C. McLaren, G. Brittenham, 1995 .

On the EM algorithm for overdispersed count data

G. McLachlan, Geoffrey J. McLachlan, 1997, Statistical methods in medical research.

Mining in the Presence of Selectivity Bias and its Application to Reject Inference

G. McLachlan, A. Feelders, Soong Chang, 1998, Knowledge Discovery and Data Mining.

Log-transformed kernel density estimation for positive data

Andrew T. Jones, G. McLachlan, H. Nguyen, 2018 .

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

G. McLachlan, Jinran Wu, You-Gan Wang, 2022, Pattern Recognit..

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

G. McLachlan, 1980 .

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 Bivariate Mixture Modeling Was Used to Analyze Joint Population Distributions of Transferrin Satu

G. McLachlan, D. Reboussin, B. Snively, 2022 .

Laplace mixture of linear experts

Geoffrey J. McLachlan, Hien Duy Nguyen, G. McLachlan, 2016, Comput. Stat. Data Anal..

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

Geoffrey J. McLachlan, S. Ganesalingam, G. McLachlan, 1980 .

Mixtures of Factor Analyzers

G. McLachlan, D. Peel, 2000, ICML.

GASTROENTEROLOGY 1998;114:543–549 Distribution of Transferrin Saturation in an Australian Population: Relevance to the Early Diagnosis

G. McLachlan, B. Leggett, C. McLaren, 2022 .

Asymptotic inference for hidden process regression models

Geoffrey J. McLachlan, Hien Duy Nguyen, G. McLachlan, 2014, 2014 IEEE Workshop on Statistical Signal Processing (SSP).

Constrained sample discrimination with the studentized classification statistic w

Geoffrey J. McLachlan, G. McLachlan, 1977 .

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

G. J. McLachlan, G. McLachlan, 1980 .

Skew-normal generalized spatial panel data model

Geoffrey J. McLachlan, Mohadeseh Alsadat Farzammehr, Mohammad Reza Zadkarami, 2019, Commun. Stat. Simul. Comput..

Harmless label noise and informative soft-labels in supervised classification

Geoffrey J. McLachlan, Daniel Ahfock, G. McLachlan, 2021, Comput. Stat. Data Anal..

Comment on "On Nomenclature, and the Relative Merits of Two Formulations of Skew Distributions" by A. Azzalini, R. Browne, M. Genton, and P. McNicholas

Geoffrey J. McLachlan, Sharon X. Lee, G. McLachlan, 2016, 1601.00773.

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

Geoffrey J. McLachlan, Sharon X. Lee, Sharon X. Lee, 2013, Stat. Methods Appl..

Mixtures of Factor Analyzers with Common Factor Loadings: Applications to the Clustering and Visualization of High-Dimensional Data

Geoffrey J. McLachlan, Jangsun Baek, Lloyd K. Flack, 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Extension of the mixture of factor analyzers model to incorporate the multivariate t-distribution

Geoffrey J. McLachlan, Richard Bean, Liat Ben-Tovim Jones, 2007, Comput. Stat. Data Anal..

High-Breakdown Linear Discriminant Analysis

Douglas M. Hawkins, Geoffrey J. McLachlan, G. McLachlan, 1997 .

Criterion for Selecting Variables for Linear Discriminant Function

Geoffrey J. McLachlan, G. McLachlan, 1976 .

Application of Multiple Imputation for Missing Values in Three-Way Three-Mode Multi-Environment Trial Data

Geoffrey J. McLachlan, Kaye E. Basford, Ting Tian, 2015, PloS one.

Standard errors of fitted component means of normal mixtures

Geoffrey J. McLachlan, Kaye E. Basford, G. McLachlan, 1997 .

An Algorithm for Fitting Mixtures of Gompertz Distributions to Censored Survival Data

G. McLachlan, D. McGiffin, A. S. Ng, 1997 .

An Enduring Interest in Classification: Supervised and Unsupervised

Geoffrey J. McLachlan, G. McLachlan, 2012, Journeys to Data Mining.

Positive Data Kernel Density Estimation via the LogKDE Package for R

Geoffrey J. McLachlan, Hien D. Nguyen, Andrew T. Jones, 2018, AusDM.

Non-asymptotic oracle inequalities for the Lasso in high-dimensional mixture of experts

G. McLachlan, H. Nguyen, TrungTin Nguyen, 2020, 2009.10622.

An l1-oracle inequality for the Lasso in mixture-of-experts regression models

G. McLachlan, H. Nguyen, TrungTin Nguyen, 2020, ArXiv.

Mixtures of Factor Analyzers with Common Factor Loadings: Applications to the Clustering and Visualization of High-Dimensional Data

G. McLachlan, Jangsun Baek, L. K. Flack, 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

An overview of skew distributions in model-based clustering

Geoffrey J. McLachlan, Sharon X. Lee, Sharon X. Lee, 2021, J. Multivar. Anal..

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

Sharon X. Lee, G. McLachlan, Tsung-I Lin, 2020, Adv. Data Anal. Classif..

Mixtures of Factor Analyzers with Fundamental Skew Symmetric Distributions

Sharon X. Lee, G. McLachlan, Tsung-I Lin, 2018, 1802.02467.

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

Geoffrey J. McLachlan, Sharon X. Lee, Sharon X. Lee, 2013, Statistical Methods & Applications.

Robust Cluster Analysis via Mixture Models

G. McLachlan, S. Ng, Richard Bean, 2022 .

Issues of robustness and high dimensionality in cluster analysis

G. McLachlan, K. Basford, Richard Bean, 2006 .

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

Geoffrey J. McLachlan, Hien D. Nguyen, Andrew T. Jones, 2018 .

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

Andrew T. Jones, G. McLachlan, H. Nguyen, 2018, Japanese Journal of Statistics and Data Science.

Iteratively-Reweighted Least-Squares Fitting of Support Vector Machines: A Majorization-Minimization Algorithm Approach pdf

Geoffrey J. McLachlan, Hien D. Nguyen, G. McLachlan, 2017, ArXiv.

Segmentation of brain MR images with bias field correction

G. McLachlan, Deming Wang, S. Ng, 2003 .

Deep Gaussian mixture models

Geoffrey J. McLachlan, Cinzia Viroli, G. McLachlan, 2017, Statistics and Computing.

Correcting for selection bias via cross-validation in the classification of microarray data

G. J. McLachlan, G. McLachlan, J. Zhu, 2008, 0805.2501.

Finite Mixture Models

G. McLachlan, D. Peel, 2000, Wiley Series in Probability and Statistics.

Estimation of Classification Rules from Partially Classified Data pdf

Geoffrey J. McLachlan, Daniel Ahfock, G. McLachlan, 2020, Data Analysis and Rationality in a Complex World.

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

Geoffrey J. McLachlan, Richard Tawiah, Shu-Kay Ng, 2019, Pattern Recognit..

PPEM: Privacy‐preserving EM learning for mixture models

Geoffrey J. McLachlan, Sharon X. Lee, Kaleb L. Leemaqz, 2019, Concurr. Comput. Pract. Exp..

Randomized mixture models for probability density approximation and estimation

Dianhui Wang, Geoffrey J. McLachlan, Hien Duy Nguyen, 2018, Inf. Sci..

Private Distributed Three-Party Learning of Gaussian Mixture Models

Geoffrey J. McLachlan, Sharon X. Lee, Kaleb L. Leemaqz, 2017, ATIS.

Faster Functional Clustering via Gaussian Mixture Models

Geoffrey J McLachlan, Jeremy F. P. Ullmann, Andrew L Janke, 2016, 1608.05481.

A Universal Approximation Theorem for Mixture-of-Experts Models

Geoffrey J. McLachlan, Hien Duy Nguyen, Luke R. Lloyd-Jones, 2016, Neural Computation.

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

Geoffrey J McLachlan, Saumyadipta Pyne, Sharon X. Lee, 2016, Cytometry. Part A : the journal of the International Society for Analytical Cytology.

Maximum likelihood estimation of Gaussian mixture models without matrix operations

Geoffrey J. McLachlan, Hien Duy Nguyen, G. McLachlan, 2015, Adv. Data Anal. Classif..

Mixture models for clustering multilevel growth trajectories

Geoffrey J. McLachlan, Shu-Kay Ng, G. McLachlan, 2014, Comput. Stat. Data Anal..

A common factor-analytic model for classification

Geoffrey J. McLachlan, Mingzhu Sun, G. McLachlan, 2013, 2013 IEEE International Conference on Bioinformatics and Biomedicine.

Multivariate Skew t Mixture Models: Applications to Fluorescence-Activated Cell Sorting Data

Kui Wang, Geoffrey J. McLachlan, Shu-Kay Ng, 2009, 2009 Digital Image Computing: Techniques and Applications.

Multilevel modeling for inference of genetic regulatory networks

Kui Wang, Geoffrey J. McLachlan, Shu-Kay Ng, 2005, SPIE Micro + Nano Materials, Devices, and Applications.

Normalized Gaussian Networks with Mixed Feature Data

Geoffrey J. McLachlan, Shu-Kay Ng, G. McLachlan, 2005, Australian Conference on Artificial Intelligence.

Cluster Analysis of High-Dimensional Data: A Case Study

Geoffrey J. McLachlan, Richard Bean, G. McLachlan, 2005, IDEAL.

Finite Mixture Models in Biostatistics

Geoffrey J. McLachlan, Sharon X. Lee, Shu-Kay Ng, 2017 .

Application of Mixture Models to Large Datasets

Geoffrey J. McLachlan, Saumyadipta Pyne, Sharon X. Lee, 2016 .

Modelling of Inter-sample Variation in Flow Cytometric Data with the Joint Clustering and Matching ( JCM ) Procedure

Geoffrey J. McLachlan, Saumyadipta Pyne, Sharon X. Lee, 2015 .

Mixture Models in Statistics

G. McLachlan, 2015 .

Mixture of regression models with latent variables and sparse coefficient parameters.

Geoffrey J. McLachlan, Shu-Kay Ng, G. McLachlan, 2014 .

Clustering of High-Dimensional and Correlated Data

Geoffrey J. McLachlan, Shu-Kay Ng, Kent Wang, 2010 .

Clustering methods for gene-expression data

G. McLachlan, L. K. Flack, 2009 .

Clustering via Mixture Regression Models with Random Effects

Geoffrey J. McLachlan, Shu Kay Angus Ng, Kui Wang, 2008 .

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

Christophe Ambroise, Liat Ben-Tovim Jones, L. B. Jones, 2005 .

On Clustering by Mixture Models

Geoffrey J. McLachlan, David Peel, Shu Kay Angus Ng, 2003 .

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

Geoffrey J. McLachlan, A. S. K. Ng, G. McLachlan, 2002 .

4 Choice of Starting Values for the EM Algorithm

G. McLachlan, S. Ng, K. Wang, 2022 .

Patient-specific analysis of sequential haematological data by multiple linear regression and mixture distribution modelling.

H C Lukaski, G J McLachlan, G. McLachlan, 2000, Statistics in medicine.

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

Geoffrey J. McLachlan, Shu-Kay Ng, G. McLachlan, 2013, 2013 IEEE International Conference on Bioinformatics and Biomedicine.

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

Christophe Ambroise, Geoffrey J. McLachlan, G. McLachlan, 2006 .

Robust Estimation in Gaussian Mixtures Using Multiresolution Kd-trees

Geoffrey J. McLachlan, Shu-Kay Ng, G. McLachlan, 2003, DICTA.

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

Geoffrey J. McLachlan, Jangsun Baek, Young Sook Son, 2007 .

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

Richard Tawiah, Geoffrey J McLachlan, Shu Kay Ng, 2020, Statistical methods in medical research.

Identifying fiber bundles with regularised к-means clustering applied to the grid-based data

Geoffrey J. McLachlan, Vladimir Nikulin, G. McLachlan, 2010, The 2010 International Joint Conference on Neural Networks (IJCNN).

Bioinformatics Original Paper a Mixture Model with Random-effects Components for Clustering Correlated Gene-expression Profiles

G. McLachlan, S. Ng, K. Wang, 2022 .

On the classification of microarray gene-expression data

G. McLachlan, K. Basford, S. Rathnayake, 2013, Briefings Bioinform..

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

D. Rubin, G. McLachlan, M. Lenzenweger, 2007, Journal of abnormal psychology.

A score test for overdispersion in zero‐inflated poisson mixed regression model

G. McLachlan, K. Yau, Liming Xiang, 2007, Statistics in medicine.

Semi-supervised Gaussian mixture modelling with a missing-data mechanism in R

G. McLachlan, Ryan Thompson, Daniel Ahfock, 2023, 2302.13206.

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

G. McLachlan, Daniel Ahfock, 2020, Statistics and computing.

A case study of two clustering methods based on maximum likelihood

Geoffrey J. McLachlan, S. Ganesalingam, G. McLachlan, 1979 .

gmmsslm : Semi-supervised Gaussian mixture modeling with a missing data mechanism in R

G. McLachlan, Ryan Thompson, Daniel Ahfock, 2023 .

Asymptotic relative efficiency of the linear discriminant function under partial nonrandom classification of the training data

Geoffrey J. McLachlan, G. McLachlan, D. Scot, 1995 .

Error rate estimation on the basis of posterior probabilities

Geoffrey J. McLachlan, S. Ganesalingam, G. McLachlan, 1980, Pattern Recognit..

Bias associated with the discriminant analysis approach to the estimation of mixing proportions

Geoffrey J. McLachlan, Charles R. O. Lawoko, G. McLachlan, 1989, Pattern Recognit..

A note on the choice of a weighting function to give an efficient method for estimating the probability of misclassification

Geoffrey J. McLachlan, G. McLachlan, 1977, Pattern Recognit..

An algorithm for the likelihood ratio test of one versus two components in a normal mixture model fitted to grouped and truncated data

G. McLachlan, C. McLaren, D. Matthews, 1995 .

On formulations of skew factor models: skew errors versus skew factors

Sharon X. Lee, G. McLachlan, 2018, 1810.04842.

The skew-t factor analysis model

Sharon X. Lee, G. McLachlan, Tsung-I Lin, 2013, 1310.5336.

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

Sharon X. Lee, G. McLachlan, 2021 .

Functional Mixtures-of-Experts

G. McLachlan, Faicel Chamroukhi, V. Hoang, 2022, ArXiv.

Statistical matching of non-Gaussian data

Geoffrey J. McLachlan, Daniel Ahfock, Saumyadipta Pyne, 2019, 1903.12342.

Unsupervised Component-Wise EM Learning for Finite Mixtures of Skew t-distributions

Geoffrey J. McLachlan, Sharon X. Lee, Sharon X. Lee, 2016, ADMA.

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

Geoffrey J. McLachlan, Sharon X. Lee, G. McLachlan, 2013 .

On the choice of the number of blocks with the incremental EM algorithm for the fitting of normal mixtures

Geoffrey J. McLachlan, Shu-Kay Ng, G. McLachlan, 2003, Stat. Comput..

On some Variants of the EM Algorithm for the Fitting of Finite Mixture Models

G. McLachlan, S. Ng, 2003 .

Clustering of magnetic resonance images

Geoffrey J. McLachlan, Deming Wang, Graham J. Galloway, 1996 .

The impact of the EM algorithm on medical statistics.

G. McLachlan, 1997, Statistical methods in medical research.

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

Sharon X. Lee, G. McLachlan, 2013, Stat. Methods Appl..

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

Sharon X. Lee, G. McLachlan, 2013, Statistical Methods & Applications.

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

G. J. McLachlan, S.-W. Ng, G. McLachlan, 2013, Biostatistics.

Mixture models for partially unclassified data: a case study of renal venous renin in hypertension.

G J McLachlan, G. McLachlan, R. Gordon, 1989, Statistics in medicine.

Maximum Likelihood Estimation for Finite Mixtures of Canonical Fundamental Skew t-Distributions: the Unification of the Unrestricted and Restricted Skew t-Mixture Models

Geoffrey J. McLachlan, Sharon X. Lee, G. McLachlan, 2014, 1401.8182.

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

Andy H. Lee, G. McLachlan, K. Yau, 2009, Statistics in medicine.

FITTING FINITE MIXTURE MODELS IN A REGRESSION CONTEXT

Geoffrey J. McLachlan, G. McLachlan, P. N. Jones, 1992 .

Robust clustering based on finite mixture of multivariate fragmental distributions

Geoffrey J. McLachlan, Mohsen Maleki, Sharon X. Lee, 2023 .

Robust estimation of mixtures of Skew Normal Distributions

Luis Angel García-Escudero, Francesca Greselin, A. Mayo-Iscar, 2016 .

A Simple Parallel EM Algorithm for Statistical Learning via Mixture Models

Geoffrey J. McLachlan, Sharon X. Lee, Kaleb L. Leemaqz, 2016, 2016 International Conference on Digital Image Computing: Techniques and Applications (DICTA).

Bioinformatics Original Paper a Mixture Model with Random-effects Components for Clustering Correlated Gene-expression Profiles

G. McLachlan, S. Ng, K. Wang, 2022 .

Further results on the effect of intraclass correlation among training samples in discriminant analysis

Geoffrey J. McLachlan, G. McLachlan, 1976, Pattern Recognit..

Asymptotic error rates of the W and Z statistics when the training observations are dependent

Geoffrey J. McLachlan, Charles R. O. Lawoko, G. McLachlan, 1986, Pattern Recognit..

Some asymptotic results on the effect of autocorrelation on the error rates of the sample linear discriminant function

Geoffrey J. McLachlan, Charles R. O. Lawoko, G. McLachlan, 1983, Pattern Recognit..

on the Criterion of Asymptotic Mean Square Error

G. McLachlan, 1974 .

The biases associated with maximum likelihood methods of estimation of the multivariate logistic risk function

Geoffrey J. McLachlan, G. McLachlan, K. Byth, 1978 .