Geoffrey J. McLachlan

发表

Geoffrey J. McLachlan, David Peel, G. McLachlan, 2005 .

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).

Selection of Variables in Discriminant-Analysis

Geoffrey J. McLachlan, G. McLachlan, 1980 .

On Clustering by Mixture Models

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

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.

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.

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

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

A common factor-analytic model for classification

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

Some Cluster Analysis Methods

Geoffrey J. McLachlan, Kim-Anh Do, Christophe Ambroise, 2005 .

Characteristic traffic load effects from a mixture of loading events on short to medium span bridges

Geoffrey J. McLachlan, Colin Christopher Caprani, Eugene J. O'Brien, 2008 .

Cleaning and Normalization

Geoffrey J. McLachlan, Kim-Anh Do, Christophe Ambroise, 2005 .

Randomized mixture models for probability density approximation and estimation

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

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

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

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..

Modelling high-dimensional data by mixtures of factor analyzers

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

Hidden Markov Models

Geoffrey J. McLachlan, David Peel, 2005 .

The EM Algorithm

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

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..

Multivariate Normal Mixtures

Geoffrey J. McLachlan, David Peel, G. McLachlan, 2005 .

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

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

Classification of Imbalanced Marketing Data with Balanced Random Sets

Geoffrey J. McLachlan, Vladimir Nikulin, 2009, KDD Cup.

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

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

Maximum likelihood estimation of Gaussian mixture models without matrix operations

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

Special issue on “New trends on model-based clustering and classification”

Gérard Govaert, Geoffrey J. McLachlan, Salvatore Ingrassia, 2015, Adv. Data Anal. Classif..

Ensemble Approach for the Classification of Imbalanced Data

Geoffrey J. McLachlan, Vladimir Nikulin, Shu-Kay Ng, 2009, Australasian Conference on Artificial Intelligence.

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

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

Monte Carlo Versions of the EM Algorithm

Geoffrey J. McLachlan, Thriyambakam Krishnan, G. McLachlan, 2007 .

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

Geoffrey J. McLachlan, Jangsun Baek, Suren I. Rathnayake, 2011 .

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..

discriminative methods Analysis of molecular profile data using generative and

Christophe Ambroise, Heping Zhang, Geoffrey J. McLachlan, 2015 .

Statistical Image Analysis

Geoffrey J. McLachlan, G. McLachlan, 2005 .

Clustering of High-Dimensional and Correlated Data

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

Automated High-Dimensional Flow Cytometric Data Analysis

Jill P. Mesirov, Geoffrey J. McLachlan, Pablo Tamayo, 2010, RECOMB.

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

Geoffrey J. McLachlan, Vladimir Nikulin, Tian-Hsiang Huang, 2010, 2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

Top 10 algorithms in data mining

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

Finite Mixture Models in Biostatistics

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

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..

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.

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

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

Linear mixed models with marginally symmetric nonparametric random effects

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

Multilevel survival modelling of recurrent urinary tract infections

Kui Wang, Geoffrey J. McLachlan, Andy H. Lee, 2007, Comput. Methods Programs Biomed..

Mixture Distributions – Further Developments

Geoffrey J. McLachlan, G. McLachlan, 2016 .

Assessing the Significance of Groups in High-Dimensional Data

Geoffrey J. McLachlan, 2010, 2010 IEEE International Conference on Data Mining.

Skew-normal Bayesian spatial heterogeneity panel data models

Geoffrey J. McLachlan, Sharon X. Lee, Mohadeseh Alsadat Farzammehr, 2019, Journal of applied statistics.

Top-10 Data Mining Case Studies

Xindong Wu, Jian Pei, Christos Faloutsos, 2012, Int. J. Inf. Technol. Decis. Mak..

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

Geoffrey J. McLachlan, Sharon X. Lee, Kaleb L. Leemaqz, 2016, IEEE Transactions on Neural Networks and Learning Systems.

Robust Cluster Analysis via Mixtures of Multivariate t-Distributions

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

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..

False Discovery Rate Control in Magnetic Resonance Imaging Studies via Markov Random Fields

Andrew L. Janke, Geoffrey J. McLachlan, Nicolas Cherbuin, 2014, IEEE Transactions on Medical Imaging.

Deep Gaussian mixture models

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

EMMIXcskew: an R Package for the Fitting of a Mixture of Canonical Fundamental Skew t-Distributions

Geoffrey J. McLachlan, Sharon X. Lee, Sharon X. Lee, 2015, 1509.02069.

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

Geoffrey J. McLachlan, Kaye E. Basford, Ting Tian, 2014 .

Maximum likelihood estimation of triangular and polygonal distributions

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

Error rate estimation on the basis of posterior probabilities

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

Multivariate mixture models for classification of anemias

Geoffrey J. McLachlan, Padhraic Smyth, Igor V. Cadez, 2000 .

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

Geoffrey J. McLachlan, David Peel, Kaye E. Basford, 1999 .

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..

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).

Clustering replicated microarray data via mixtures of random effects models for various covariance structures

Geoffrey J. McLachlan, Shu Kay Angus Ng, Richard Bean, 2006 .

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..

Normalized Gaussian Networks with Mixed Feature Data

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

Multi-Node EM Algorithm for Finite Mixture Models

Geoffrey J. McLachlan, Sharon X. Lee, Kaleb L. Leemaqz, 2020 .

Clustering of magnetic resonance images

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

Integrative mixture of experts to combine clinical factors and gene markers

Geoffrey J. McLachlan, Kim-Anh Lê Cao, Emmanuelle Meugnier, 2010, Bioinform..

Private Distributed Three-Party Learning of Gaussian Mixture Models

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

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

Geoffrey J. McLachlan, David Peel, 1998, Proceedings. Fourteenth International Conference on Pattern Recognition (Cat. No.98EX170).

Whole-Volume Clustering of Time Series Data from Zebrafish Brain Calcium Images via Mixture Model-Based Functional Data Analysis

Andrew L. Janke, Geoffrey J. McLachlan, David C. Reutens, 2016 .

Merging Algorithm to Reduce Dimensionality in Application to Web-Mining

Geoffrey J. McLachlan, Vladimir Nikulin, 2007, Australian Conference on Artificial Intelligence.

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..

Discrimination with autocorrelated observations

Geoffrey J. McLachlan, Charles R. O. Lawoko, 1985, Pattern Recognit..

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.

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.

Likelihood-based approaches to pattern recognition

Geoffrey J. McLachlan, G. McLachlan, 1996 .

Clustering of Microarray Data via Mixture Models

Geoffrey J. McLachlan, Richard Bean, Angus S. K. Ng, 2008 .

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

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

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

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

Clustering of High-Dimensional Data via Finite Mixture Models

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

Mixture Analysis of Directional Data

Geoffrey J. McLachlan, David Peel, G. McLachlan, 2005 .

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

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

The 2nd special issue on advances in mixture models

Geoffrey J. McLachlan, Paul D. McNicholas, Christian Hennig, 2014, Computational Statistics & Data Analysis.

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..

Hierarchical Models for Screening of Iron Deficiency Anemia

Geoffrey J. McLachlan, Padhraic Smyth, Igor V. Cadez, 1999, ICML.

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

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

On the Gradient-based Algorithm for Matrix Factorization Applied to Dimensionality Reduction

Geoffrey J. McLachlan, Vladimir Nikulin, 2010, BIOINFORMATICS.

PPEM: Privacy‐preserving EM learning for mixture models

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

Analyzing Microarray Gene Expression Data

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

Maximum likelihood clustering via normal mixture models

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

Mixtures - Models and Applications

Geoffrey J. McLachlan, G. McLachlan, 2019, The Exponential Distribution.

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

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

Mixture Models for Detecting Differentially Expressed Genes in Microarrays

Geoffrey J. McLachlan, Richard Bean, Liat Ben-Tovim Jones, 2006, Int. J. Neural Syst..

Statistical Analysis on Microarray Data: Selection of Gene Prognosis Signatures

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

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

Geoffrey J. McLachlan, Padhraic Smyth, Igor V. Cadez, 2002, Machine Learning.

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.

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.

logKDE: log-transformed kernel density estimation

Geoffrey J. McLachlan, Hien Duy Nguyen, Andrew T. Jones, 2018, J. Open Source Softw..

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..

Estimation of Error Rates

Geoffrey J. McLachlan, G. McLachlan, 2005 .

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..

Positive Data Kernel Density Estimation via the LogKDE Package for R

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

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..

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

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

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

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

On the classification of microarray gene-expression data

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

Finding group structures in “Big Data” in healthcare research using mixture models

Geoffrey J. McLachlan, Shu-Kay Ng, 2016, 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

Statistical matching of non-Gaussian data

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

A Very Fast Algorithm for Matrix Factorization pdf

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

Wallace's Approach to Unsupervised Learning: The Snob Program

Geoffrey J. McLachlan, Murray A. Jorgensen, G. McLachlan, 2008, Comput. J..

A Universal Approximation Theorem for Mixture-of-Experts Models

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

Discriminant Analysis and Statistical Pattern Recognition: McLachlan/Discriminant Analysis & Pattern Recog

Geoffrey J. McLachlan, G. McLachlan, 2005 .

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

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

Mixture Models for Failure‐Time Data

Geoffrey J. McLachlan, David Peel, G. McLachlan, 2005 .

Laplace mixture of linear experts

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

ML Fitting of Mixture Models

Geoffrey J. McLachlan, David Peel, G. McLachlan, 2005 .

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

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

Clustering via Mixture Regression Models with Random Effects

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

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..

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

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

Computing issues for the EM algorithm in mixture models

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

Finite Mixture Models

Geoffrey J. McLachlan, David Peel, Sharon X. Lee, 2019, Annual Review of Statistics and Its Application.

Appendix: Mixture Software

Geoffrey J. McLachlan, David Peel, 2005 .

Mixture models for clustering multilevel growth trajectories

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

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 .

On the Simultaneous Use of Clinical and Microarray Expression Data in the Cluster Analysis of Tissue Samples

Christophe Ambroise, Geoffrey J. McLachlan, Justin Xi Zhu, 2004, APBC.

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

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

Application of Mixture Models to Detect Differentially Expressed Genes

Geoffrey J. McLachlan, Richard Bean, Liat Ben-Tovim Jones, 2005, IDEAL.

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..

A Block Minorization–Maximization Algorithm for Heteroscedastic Regression

Geoffrey J. McLachlan, Hien Duy Nguyen, Luke R. Lloyd-Jones, 2016, IEEE Signal Processing Letters.

Mixtures of Factor Analyzers

Geoffrey J. McLachlan, David Peel, G. McLachlan, 2000, International Conference on Machine Learning.

Mixtures of spatial spline regressions for clustering and classification

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

On a Resampling Approach to Choosing the Number of Components in Normal Mixture Models

Geoffrey J. McLachlan, David Peel, G. McLachlan, 2007 .

Discriminant analysis

Geoffrey J. McLachlan, G. McLachlan, 2012 .

An Enduring Interest in Classification: Supervised and Unsupervised

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

Mixture models : inference and applications to clustering

Geoffrey J. McLachlan, Kaye E. Basford, G. McLachlan, 1989 .

Further Applications of the EM Algorithm

Geoffrey J. McLachlan, Thriyambakam Krishnan, G. McLachlan, 2007 .

Robust Estimation in Gaussian Mixtures Using Multiresolution Kd-trees

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

Model-Based Clustering of Functional Data via Gaussian Mixture Models

Andrew L. Janke, Geoffrey J. McLachlan, Hien D. Nguyen, 2016 .

CRRAO Advanced Institute of Mathematics, Statistics and Computer Science (AIMSCS)

Jill P. Mesirov, Geoffrey J. McLachlan, Sharon X. Lee, 2013 .

Use of Mixture Models in Multiple Hypothesis Testing with Applications in Bioinformatics

Geoffrey J. McLachlan, Leesa Wockner, 2010 .

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

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

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.

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

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

Multilevel modeling for inference of genetic regulatory networks

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

Further results on discrimination with autocorrelated observations

Geoffrey J. McLachlan, Charles R. O. Lawoko, 1988, Pattern Recognit..

Spatial False Discovery Rate Control for Magnetic Resonance Imaging Studies

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

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

Geoffrey J. McLachlan, A. J. Feelders, Soong Chang, 1998, KDD.

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

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

Asymptotic inference for hidden process regression models

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

Robust mixture modelling using the t distribution

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

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.

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.

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..

Neural Networks and Related Methods for Classification - Discussion

Trevor Hastie, Robert Tibshirani, David J. Hand, 1994 .

Cluster Analysis of High-Dimensional Data: A Case Study

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

Modelling a Particular Philatelic Mixture Using the Mixture Likelihood Method of Clustering

Geoffrey J. McLachlan, Kaye E. Basford, 1992 .

Partial identification in the statistical matching problem

Geoffrey J. McLachlan, Sharon X. Lee, Daniel Ahfock, 2016, Comput. Stat. Data Anal..

Skew-normal generalized spatial panel data model

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

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

Geoffrey J. McLachlan, Mohsen Mohammadzadeh, Mohadeseh Alsadat Farzammehr, 2021, Communications in Statistics - Theory and Methods.

Multi‐node Expectation–Maximization algorithm for finite mixture models

Geoffrey J. McLachlan, Kaleb L. Leemaqz, Sharon X. Lee, 2021, Stat. Anal. Data Min..

Semi-Supervised Learning of Classifiers from a Statistical Perspective: A Brief Review pdf

Geoffrey J. McLachlan, Daniel Ahfock, G. McLachlan, 2021, Econometrics and Statistics.

An overview of skew distributions in model-based clustering

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

Harmless label noise and informative soft-labels in supervised classification

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

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

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

Robust clustering based on finite mixture of multivariate fragmental distributions

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

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.

Automated Gating and Dimension Reduction of High-Dimensional Cytometry Data

Geoffrey J. McLachlan, Saumyadipta Pyne, Sharon X. Lee, 2021 .

Data fusion using factor analysis and low-rank matrix completion

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

The mixture method of clustering applied to three-way data

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

SOME EXPECTED VALUES FOR THE ERROR RATES OF THE SAMPLE QUADRATIC DISCRIMINANT FUNCTION1

Geoffrey J. McLachlan, G. McLachlan, 1975 .

High-Breakdown Linear Discriminant Analysis

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

Supervised Classification of Flow Cytometric Samples via the Joint Clustering and Matching (JCM) Procedure

Geoffrey J. McLachlan, Saumyadipta Pyne, Sharon X. Lee, 2014, 1411.2820.

Clustering methods for gene-expression data

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

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.

A case study of two clustering methods based on maximum likelihood

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

Estimation of Mixing Proportions: A Case Study

Geoffrey J. McLachlan, K. Do, G. McLachlan, 1984 .

Modelling mass−size particle data by finite mixtures

Geoffrey J. McLachlan, G. McLachlan, P. N. Jones, 1989 .

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

Geoffrey J. McLachlan, 1988 .

Constrained sample discrimination with the studentized classification statistic w

Geoffrey J. McLachlan, G. McLachlan, 1977 .

Comment on "Comparing two formulations of skew distributions with special reference to model-based clustering" by A. Azzalini, R. Browne, M. Genton, and P. McNicholas

Geoffrey J. McLachlan, Sharon X. Lee, G. McLachlan, 2014, 1404.1733.

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 .

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

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

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

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

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 .

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

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

Mixture models for standard p-dimensional Euclidean data

Geoffrey J. McLachlan, Suren I. Rathnayake, 2015 .

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 .

Expected Error Rates for Logistic Regression Versus Normal Discriminant Analysis

Geoffrey J. McLachlan, G. McLachlan, K. Byth, 1979 .

Cluster analysis in a randomized complete block design

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

Criterion for Selecting Variables for Linear Discriminant Function

Geoffrey J. McLachlan, G. McLachlan, 1976 .

Application of Mixture Models to Large Datasets

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

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

Geoffrey J. McLachlan, S. Ganesalingam, Charles R. O. Lawoko, 1982 .

Laplace-normal mixtures fitted to wind shear data

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

On missing label patterns in semi-supervised learning

Geoffrey J. McLachlan, Daniel Ahfock, G. McLachlan, 2019, 1904.02883.

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

Geoffrey J. McLachlan, S. Ganesalingam, G. McLachlan, 1981 .

FITTING FINITE MIXTURE MODELS IN A REGRESSION CONTEXT

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

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

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

Microarrays in Gene Expression Studies

Christophe Ambroise, Geoffrey J. McLachlan, K. Do, 2005 .

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.

Multivariate analysis: Classification and discriminant analysis

Geoffrey J. McLachlan, G. McLachlan, 2001 .

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

Geoffrey J. McLachlan, Saumyadipta Pyne, G. McLachlan, 2015 .

On a general method for matrix factorisation applied to supervised classification

Geoffrey J. McLachlan, Vladimir Nikulin, 2009, 2009 IEEE International Conference on Bioinformatics and Biomedicine Workshop.

Using mixture models to detect differentially expressed genes

Geoffrey J. McLachlan, Richard Bean, L. B. Jones, 2005 .

A Note on the Aitkin‐Rubin Approach to Hypothesis Testing in Mixture Models

Geoffrey J. McLachlan, Barry G. Quinn, G. McLachlan, 1987 .

On the bias and variance of some proportion estimators

Geoffrey J. McLachlan, G. McLachlan, 1982 .

The bias of sample based posterior probabilities

Geoffrey J. McLachlan, G. McLachlan, 1977 .

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

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

Discrimination via Normal Models

Geoffrey J. McLachlan, G. McLachlan, 2005 .

Supervised Classification of Tissue Samples

Christophe Ambroise, Geoffrey J. McLachlan, K. Do, 2005 .

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

Geoffrey J. McLachlan, Shu-Kay Ng, G. McLachlan, 1998 .

Standard errors of fitted component means of normal mixtures

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