Geoffrey McLachlan

Geoffrey McLachlan's AcademicInfluence.com Rankings

Geoffrey McLachlan

Mathematics

#3424

World Rank

#5054

Historical Rank

Statistics

#167

World Rank

#215

Historical Rank

mathematics Degrees

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Mathematics

Why Is Geoffrey McLachlan Influential?

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According to Wikipedia, Geoffrey John McLachlan FAA is an Australian researcher in computational statistics, machine learning and pattern recognition. McLachlan is best known for his work in classification and finite mixture models. He is the joint author of five influential books on the topics of mixtures and classification, as well as their applications. Currently, McLachlan is a Professor of statistics within the School of Mathematics and Physics at the University of Queensland.

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Geoffrey McLachlan's Published Works

Number of citations in a given year to any of this author's works

Total number of citations to an author for the works they published in a given year. This highlights publication of the most important work(s) by the author

Published Works

Finite Mixture Models (2000) (7709)
The EM algorithm and extensions (1996) (6305)
Top 10 algorithms in data mining (2007) (5183)
Discriminant Analysis and Statistical Pattern Recognition (1992) (2904)
Mixture models : inference and applications to clustering (1989) (2395)
Selection bias in gene extraction on the basis of microarray gene-expression data (2002) (1437)
Robust mixture modelling using the t distribution (2000) (884)
The EM Algorithm and Extensions: Second Edition (2008) (805)
On Bootstrapping the Likelihood Ratio Test Statistic for the Number of Components in a Normal Mixture (1987) (785)
Analyzing Microarray Gene Expression Data (2004) (681)
A mixture model-based approach to the clustering of microarray expression data (2002) (593)
Discriminant Analysis and Statistical Pattern Recognition: McLachlan/Discriminant Analysis & Pattern Recog (2005) (523)
Automated high-dimensional flow cytometric data analysis (2009) (434)
Modelling high-dimensional data by mixtures of factor analyzers (2003) (283)
Conservation and divergence in Toll-like receptor 4-regulated gene expression in primary human versus mouse macrophages (2012) (278)
Finite mixtures of multivariate skew t-distributions: some recent and new results (2014) (237)
Mixtures of Factor Analyzers (2000) (226)
Pattern Classification: A Unified View of Statistical and Neural Approaches. (1998) (216)
The EMMIX software for the fitting of mixtures of normal and t-components (1999) (215)
On mixtures of skew normal and skew $$t$$-distributions (2012) (209)
Multi-level zero-inflated Poisson regression modelling of correlated count data with excess zeros (2006) (202)
Robust Cluster Analysis via Mixtures of Multivariate t-Distributions (1998) (195)
A simple implementation of a normal mixture approach to differential gene expression in multiclass microarrays (2006) (184)
A Mixture model with random-effects components for clustering correlated gene-expression profiles (2006) (165)
Extension of the mixture of factor analyzers model to incorporate the multivariate t-distribution (2007) (162)
Iterative Reclassification Procedure for Constructing An Asymptotically Optimal Rule of Allocation in Discriminant-Analysis (1975) (155)
Mixtures of Factor Analyzers with Common Factor Loadings: Applications to the Clustering and Visualization of High-Dimensional Data (2010) (154)
The EMMIX Algorithm for the Fitting of Normal and t-Components (1999) (153)
Cluster analysis and related techniques in medical research (1992) (146)
Allograft Aortic Valve Replacement: Long‐Term Comparative Clinical Analysis of the Viable Cryopreserved and Antibiotic 4°C Stored Valves (1991) (146)
Aortic valve infection. Risk factors for death and recurrent endocarditis after aortic valve replacement. (1992) (142)
Fitting mixture models to grouped and truncated data via the EM algorithm. (1988) (142)
Distribution of transferrin saturation in an Australian population: relevance to the early diagnosis of hemochromatosis. (1998) (139)
High-Breakdown Linear Discriminant Analysis (1997) (130)
On the number of components in a Gaussian mixture model (2014) (124)
9 The classification and mixture maximum likelihood approaches to cluster analysis (1982) (117)
Model-based clustering and classification with non-normal mixture distributions (2013) (111)
EMMIXuskew: An R Package for Fitting Mixtures of Multivariate Skew t Distributions via the EM Algorithm (2012) (103)
Standard errors of fitted component means of normal mixtures (1997) (101)
The efficiency of a linear discriminant function based on unclassified initial samples (1978) (91)
Fitting Mixtures of Kent Distributions to Aid in Joint Set Identification (2001) (86)
Extensions of the EM Algorithm (2007) (85)
Extending mixtures of factor models using the restricted multivariate skew-normal distribution (2013) (83)
Mixtures of common t-factor analyzers for clustering high-dimensional microarray data (2011) (81)
The mixture method of clustering applied to three-way data (1985) (80)
Resolving the latent structure of schizophrenia endophenotypes using expectation-maximization-based finite mixture modeling. (2007) (79)
FITTING FINITE MIXTURE MODELS IN A REGRESSION CONTEXT (1992) (78)
Finite mixtures of canonical fundamental skew $$t$$t-distributions (2014) (78)
Neural Networks and Related Methods for Classification - Discussion (1994) (75)
Advances in Data Analysis and Classification (2015) (75)
Finite mixtures of canonical fundamental skew t-distributions - The unification of the restricted and unrestricted skew t-mixture models (2014) (71)
An Asymptotic Unbiased Technique for Estimating the Error Rates in Discriminant Analysis (1974) (70)
A score test for overdispersion in zero‐inflated poisson mixed regression model (2007) (65)
Deep Gaussian mixture models (2017) (65)
Likelihood Estimation with Normal Mixture Models (1985) (63)
Using the EM algorithm to train neural networks: misconceptions and a new algorithm for multiclass classification (2004) (61)
Mixture modelling for cluster analysis (2004) (61)
Comprehensive chemometrics: chemical and biochemical data analysis (2020) (60)
On the role of finite mixture models in survival analysis (1994) (60)
Updating a discriminant function in basis of unclassified data (1982) (60)
Multivariate Skew t Mixture Models: Applications to Fluorescence-Activated Cell Sorting Data (2009) (59)
Estimating the Linear Discriminant Function from Initial Samples Containing a Small Number of Unclassified Observations (1977) (58)
Maximum Likelihood Estimation of Mixture Densities for Binned and Truncated Multivariate Data (2002) (53)
On the EM algorithm for overdispersed count data (1997) (52)
Modelling the distribution of ischaemic stroke‐specific survival time using an EM‐based mixture approach with random effects adjustment (2004) (52)
Mahalanobis distance (1999) (50)
Joint Modeling and Registration of Cell Populations in Cohorts of High-Dimensional Flow Cytometric Data (2013) (49)
Criterion for Selecting Variables for Linear Discriminant Function (1976) (48)
Laplace mixture of linear experts (2016) (47)
On the fitting of mixtures of multivariate skew t-distributions via the EM algorithm (2011) (47)
The bias of the apparent error rate in discriminant analysis (1976) (44)
Algorithm AS 254: maximum likelihood estimation from grouped and truncated data with finite normal mixture models (1990) (43)
Small sample results for a linear discriminant function estimated from a mixture of normal populations (1979) (42)
On the choice of the number of blocks with the incremental EM algorithm for the fitting of normal mixtures (2003) (42)
A score test for zero‐inflation in correlated count data (2006) (41)
Estimation of the Errors of Misclassification on the Criterion of Asymptotic Mean Square Error (1974) (39)
Asymptotic Results for Discriminant Analysis When the Initial Samples are Misclassified (1972) (38)
Speeding up the EM algorithm for mixture model-based segmentation of magnetic resonance images (2004) (37)
Laplace-normal mixtures fitted to wind shear data (1990) (37)
Selection of Variables in Discriminant-Analysis (1980) (37)
EMMIXcskew: an R Package for the Fitting of a Mixture of Canonical Fundamental Skew t-Distributions (2015) (37)
An analysis of valve re-replacement after aortic valve replacement with biologic devices. (1997) (37)
Extension of mixture-of-experts networks for binary classification of hierarchical data (2007) (37)
A Note on the Aitkin‐Rubin Approach to Hypothesis Testing in Mixture Models (1987) (37)
Integrative mixture of experts to combine clinical factors and gene markers (2010) (37)
Confidence intervals for the conditional probability of misallocation in discriminant analysis. (1975) (36)
Autoantibody profiling to identify biomarkers of key pathogenic pathways in mucinous ovarian cancer. (2010) (36)
Approximation by finite mixtures of continuous density functions that vanish at infinity (2019) (35)
Error Rate Estimation in Discriminant Analysis: Recent Advances (1987) (34)
Mixture models for clustering multilevel growth trajectories (2014) (34)
ASSESSING THE PERFORMANCE OF AN ALLOCATION RULE (1986) (33)
Estimation of Allocation Rates in a Cluster Analysis Context (1985) (32)
A Note On Bias Correction in Maximum Likelihood Estimation with Logistic Discrimination (1980) (32)
A Universal Approximation Theorem for Mixture-of-Experts Models (2016) (32)
EM Algorithm (2022) (31)
A robust factor analysis model using the restricted skew-$$t$$t distribution (2015) (31)
Comment on "On Nomenclature, and the Relative Merits of Two Formulations of Skew Distributions" by A. Azzalini, R. Browne, M. Genton, and P. McNicholas (2016) (31)
Some Efficiency Results for the Estimation of the Mixing Proportion in a Mixture of 2 Normal-Distributions (1981) (29)
AN ASYMPTOTIC EXPANSION OF THE EXPECTATION OF THE ESTIMATED ERROR RATE IN DISCRIMINANT ANALYSIS1 (1973) (28)
On the choice of starting values for the EM algorithm in fitting mixture models (1988) (28)
Ensemble Approach for the Classification of Imbalanced Data (2009) (27)
An analysis of risk factors for death and mode-specific death after aortic valve replacement with allograft, xenograft, and mechanical valves. (1993) (27)
On approximations via convolution-defined mixture models (2016) (27)
Modelling the distribution of stamp paper thickness via finite normal mixtures: The 1872 Hidalgo stamp issue of Mexico revisited (1997) (27)
An incremental EM-based learning approach for on-line prediction of hospital resource utilization (2006) (27)
A Very Fast Algorithm for Matrix Factorization (2010) (27)
Discrimination with autocorrelated observations (1985) (26)
Modeling of inter‐sample variation in flow cytometric data with the joint clustering and matching procedure (2016) (26)
Microarray data analysis for differential expression: a tutorial. (2009) (25)
Robust mixtures of factor analysis models using the restricted multivariate skew-t distribution (2018) (25)
The asymptotic distributions of the conditional error rate and risk in discriminant analysis (1974) (25)
A globally convergent algorithm for lasso-penalized mixture of linear regression models (2016) (24)
Clustering objects on subsets of attributes (2004) (23)
MIXFIT: an algorithm for the automatic fitting and testing of normal mixture models (1998) (23)
Maximum likelihood estimation of Gaussian mixture models without matrix operations (2015) (22)
Estimation of Mixing Proportions: A Case Study (1984) (22)
Classification of Imbalanced Marketing Data with Balanced Random Sets (2009) (22)
On selection biases with prediction rules formed from gene expression data (2008) (22)
A case study of two clustering methods based on maximum likelihood (1979) (22)
On a Resampling Approach to Choosing the Number of Components in Normal Mixture Models (2007) (21)
Maximum likelihood clustering via normal mixture models (1996) (21)
The efficiency of Efron's “Bootstrap” Approach Applied to Error Rate Estimation in Discriminant Analysis (1980) (21)
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 (1974) (21)
Clustering of time-course gene expression profiles using normal mixture models with autoregressive random effects (2012) (20)
On some Variants of the EM Algorithm for the Fitting of Finite Mixture Models (2003) (20)
On the Relationship between the F Test and the Overall Error Rate for Variable Selection in Two-Group Discriminant Analysis (1980) (20)
Comments on: Augmenting the bootstrap to analyze high dimensional genomic data (2008) (19)
Maternity Length of Stay Modelling by Gamma Mixture Regression with Random Effects (2007) (19)
On a resampling approach for tests on the number of clusters with mixture model-based clustering of tissue samples (2004) (19)
A note on the choice of a weighting function to give an efficient method for estimating the probability of misclassification (1977) (19)
Mini-batch learning of exponential family finite mixture models (2019) (18)
Hierarchical Models for Screening of Iron Deficiency Anemia (1999) (18)
Correcting for selection bias via cross-validation in the classification of microarray data (2008) (18)
Error rate estimation on the basis of posterior probabilities (1980) (18)
Mixture models for partially unclassified data: a case study of renal venous renin in hypertension. (1989) (18)
Use of microarray data via model-based classification in the study and prediction of survival from lung cancer (2005) (17)
Using mixture models to detect differentially expressed genes (2005) (17)
Improving the convergence rate of the em algorithm for a mixture model fitted to grouped truncated data (1992) (17)
Some asymptotic results on the effect of autocorrelation on the error rates of the sample linear discriminant function (1983) (17)
Multilevel survival modelling of recurrent urinary tract infections (2007) (16)
Selection bias in working with the top genes in supervised classification of tissue samples (2006) (16)
Model-Based Clustering in Gene Expression Microarrays: An Application to Breast Cancer Data (2003) (16)
On the classification of microarray gene-expression data (2013) (16)
On modifications to the long-term survival mixture model in the presence of competing risks (1998) (16)
The biases associated with maximum likelihood methods of estimation of the multivariate logistic risk function (1978) (16)
A Comparison of the Mixture and Classification Approaches to Cluster-Analysis (1980) (15)
Approximations of conditional probability density functions in Lebesgue spaces via mixture of experts models (2020) (15)
Approximation of probability density functions via location-scale finite mixtures in Lebesgue spaces (2020) (15)
Mixtures of Factor Analyzers with Common Factor Loadings for the Clustering and Visualisation of High-Dimensional Data (2008) (15)
Mixtures of spatial spline regressions for clustering and classification (2016) (15)
False Discovery Rate Control in Magnetic Resonance Imaging Studies via Markov Random Fields (2014) (15)
Commentary on Steinley and Brusco (2011): recommendations and cautions. (2011) (15)
On Clustering by Mixture Models (2003) (15)
Assessing the Number of Components in Mixture Models (2005) (14)
Multivariate Normal Mixtures (2005) (14)
Penalized Principal Component Analysis of Microarray Data (2009) (14)
Fitting mixture distributions to phenylthiocarbamide (PTC) sensitivity. (1991) (14)
The 2nd special issue on advances in mixture models (2014) (14)
Parametric estimation in a genetic mixture model with application to nuclear family data. (1994) (14)
Modelling mass−size particle data by finite mixtures (1989) (14)
Clustering of magnetic resonance images (1996) (14)
AN ASYMPTOTIC EXPANSION FOR THE VARIANCE OF THE ERRORS OF MISCLASSIFICATION OF THE LINEAR DISCRIMINANT FUNCTION1 (1972) (13)
A Simple Parallel EM Algorithm for Statistical Learning via Mixture Models (2016) (13)
On a general method for matrix factorisation applied to supervised classification (2009) (13)
Inference on differences between classes using cluster-specific contrasts of mixed effects. (2013) (13)
Heterogeneity in schizophrenia; mixture modelling of age‐at‐first‐admission, gender and diagnosis (2000) (12)
The bias of sample based posterior probabilities (1977) (12)
LOGISTIC REGRESSION COMPARED TO NORMAL DISCRIMINATION FOR NON-NORMAL POPULATIONS‘ (1980) (12)
Segmentation and intensity estimation of microarray images using a gamma-t mixture model (2007) (12)
A score test for assessing the cured proportion in the long‐term survivor mixture model (2009) (12)
Estimation of Error Rates (2005) (12)
Unsupervised pattern recognition of mixed data structures with numerical and categorical features using a mixture regression modelling framework (2019) (11)
Segmentation of brain MR images with bias field correction (2003) (11)
Further results on the effect of intraclass correlation among training samples in discriminant analysis (1976) (11)
An Algorithm for Fitting Mixtures of Gompertz Distributions to Censored Survival Data (1997) (11)
Bias of Apparent Error Rate in Discriminant-Analysis (1976) (11)
Spatial clustering of time series via mixture of autoregressions models and Markov random fields (2016) (11)
Modelling asset return using multivariate asymmetric mixture models with applications to estimation of Value-at-Risk (2013) (11)
Asymptotic error rates of the W and Z statistics when the training observations are dependent (1986) (11)
Laplace mixture autoregressive models (2016) (11)
Two-component Poisson mixture regression modelling of count data with bivariate random effects (2007) (11)
Nature and man: the goal of bio-security in the course of rapid and inevitable human development (2015) (10)
A comparison of the estimative and predictive methods of estimating posterior probabilities (1979) (10)
A Block EM Algorithm for Multivariate Skew Normal and Skew $t$ -Mixture Models (2016) (10)
Maximum Likelihood Estimation for Finite Mixtures of Canonical Fundamental Skew t-Distributions: the Unification of the Unrestricted and Restricted Skew t-Mixture Models (2014) (10)
SOME EXPECTED VALUES FOR THE ERROR RATES OF THE SAMPLE QUADRATIC DISCRIMINANT FUNCTION1 (1975) (10)
The skew-t factor analysis model (2013) (10)
Utilising convolutional neural networks to perform fast automated modal mineralogy analysis for thin-section optical microscopy (2021) (10)
Cluster analysis in a randomized complete block design (1985) (9)
EMMIX-uskew: An R Package for Fitting Mixtures of Multivariate Skew t-distributions via the EM Algorithm (2012) (9)
Statistical Analysis on Microarray Data: Selection of Gene Prognosis Signatures (2009) (9)
Corruption-Resistant Privacy Preserving Distributed EM Algorithm for Model-Based Clustering (2017) (9)
Asymptotic relative efficiency of the linear discriminant function under partial nonrandom classification of the training data (1995) (9)
Constrained sample discrimination with the studentized classification statistic w (1977) (9)
Maximum Pseudolikelihood Estimation for Model-Based Clustering of Time Series Data (2016) (9)
ML Fitting of Mixture Models (2005) (9)
logKDE: log-transformed kernel density estimation (2018) (8)
Resolving the latent structure of schizophrenia endophenotypes using em-based finite mixture modeling (2007) (8)
Wallace's Approach to Unsupervised Learning: The Snob Program (2008) (8)
Mixtures of factor analysers for the analysis of high-dimensional data (2011) (8)
Mixtures of Factor Analyzers with Fundamental Skew Symmetric Distributions (2018) (8)
Maximum likelihood estimation of triangular and polygonal distributions (2016) (8)
Randomized mixture models for probability density approximation and estimation (2018) (8)
A bivariate joint frailty model with mixture framework for survival analysis of recurrent events with dependent censoring and cure fraction (2019) (7)
On missing label patterns in semi-supervised learning (2019) (7)
Patient-specific analysis of sequential haematological data by multiple linear regression and mixture distribution modelling. (2000) (7)
Expected Error Rates for Logistic Regression Versus Normal Discriminant Analysis (1979) (7)
On the bias and variance of some proportion estimators (1982) (7)
Discriminant analysis (2012) (7)
An l1-oracle inequality for the Lasso in mixture-of-experts regression models (2020) (7)
Supervised Classification of Flow Cytometric Samples via the Joint Clustering and Matching (JCM) Procedure (2014) (7)
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 (2019) (7)
Expert networks with mixed continuous and categorical feature variables: A location modeling approach. (2010) (6)
On Aitken's Method And Other Approaches For Accelerating Convergence Of The EM Algorithm (1997) (6)
A tutorial in genetic epidemiology and some considerations in statistical modeling. (2007) (6)
Iteratively-Reweighted Least-Squares Fitting of Support Vector Machines: A Majorization-Minimization Algorithm Approach (2017) (6)
Influence of patient age and implantation technique on the probability of re-replacement of the homograft aortic valve. (2002) (6)
Discriminant Analysis and Statistical Pattern Recognition.@@@Fundamentals of Pattern Recognition. (1994) (6)
Utilising a deep neural network as a surrogate model to approximate phenomenological models of a comminution circuit for faster simulations (2021) (6)
Partial identification in the statistical matching problem (2016) (6)
LARGE-SCALE SIMULTANEOUS INFERENCE WITH APPLICATIONS TO THE DETECTION OF DIFFERENTIAL EXPRESSION WITH MICROARRAY DATA (2008) (6)
Mixture cure models with time-varying and multilevel frailties for recurrent event data (2020) (6)
Mixture Models for Detecting Differentially Expressed Genes in Microarrays (2006) (6)
Bias associated with the discriminant analysis approach to the estimation of mixing proportions (1989) (6)
Application of Mixture Models to Large Datasets (2016) (6)
Mathematical classification and clustering. (1998) (6)
Whole‐volume clustering of time series data from zebrafish brain calcium images via mixture modeling (2016) (5)
Top-10 Data Mining Case Studies (2012) (5)
Application of Gene Shaving and Mixture Models to Cluster Microarray Gene Expression Data (2007) (5)
Mixture models for standard p-dimensional Euclidean data (2015) (5)
Clustering replicated microarray data via mixtures of random effects models for various covariance structures (2006) (5)
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. (2008) (5)
Asymptotic inference for hidden process regression models (2014) (5)
Constrained mixture models in competing risks problems (1999) (5)
An algorithm for the likelihood ratio test of one versus two components in a normal mixture model fitted to grouped and truncated data (1995) (5)
Identifying fiber bundles with regularised к-means clustering applied to the grid-based data (2010) (5)
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 (2014) (5)
Mixtures of factor analyzers with scale mixtures of fundamental skew normal distributions (2020) (5)
Regularised k-means clustering for dimension reduction applied to supervised classification (2009) (4)
Standard Errors of Fitted Component Means ofNormal (2007) (4)
Modelling of Inter-sample Variation in Flow Cytometric Data with the Joint Clustering and Matching ( JCM ) Procedure (2015) (4)
Clustering of gene expression data via normal mixture models. (2013) (4)
Computing issues for the EM algorithm in mixture models (1999) (4)
Clustering of High-Dimensional and Correlated Data (2010) (4)
Use of Mixture Models in Multiple Hypothesis Testing with Applications in Bioinformatics (2010) (4)
Mixture of time‐dependent growth models with an application to blue swimmer crab length‐frequency data (2016) (4)
Assessing the adequacy of Weibull survival models: a simulated envelope approach (2011) (4)
Finite mixtures of multivariate skew t-distributions: some recent and new results (2012) (4)
Statistical Image Analysis (2005) (4)
Multilevel model with random effects for clustered survival data with multiple failure outcomes (2018) (4)
PPEM: Privacy‐preserving EM learning for mixture models (2019) (4)
Clustering of Microarray Data via Mixture Models (2008) (4)
Clustering via Mixture Regression Models with Random Effects (2008) (3)
Mining in the Presence of Selectivity Bias and its Application to Reject Inference (1998) (3)
Semi-Supervised Learning of Classifiers from a Statistical Perspective: A Brief Review (2021) (3)
Scale Mixture Distribution (2019) (3)
Special issue on “New trends on model-based clustering and classification” (2015) (3)
On the Likelihood Ratio Test for Compound Distributions for Homogeneity of Mixing Proportions (1982) (3)
Robust clustering based on finite mixture of multivariate fragmental distributions (2021) (3)
Mixture Models in Statistics (2015) (3)
High Breakdown Linear Discriminant (1997) (3)
Clustering of High-Dimensional Data via Finite Mixture Models (2008) (3)
The impact of the EM algorithm on medical statistics. (1997) (3)
Chunked-and-averaged estimators for vector parameters (2016) (3)
The errors of allocation and their estimators in the two-population discrimination problem (1973) (3)
Mathematics and Statistics for the Bio-Sciences - Eason,g, Coles,cw, Gettinby,g (1981) (3)
Progress on a conjecture regarding the triangular distribution (2016) (3)
Mixtures of factor analyzers for the analysis of high-dimensional data (2011) (3)
Rejoinder to the discussion of “Model-based clustering and classification with non-normal mixture distributions” (2013) (3)
Risk Measures Based on Multivariate Skew Normal and Skew t ‐Mixture Models (2018) (3)
Application of Multiple Imputation for Missing Values in Three-Way Three-Mode Multi-Environment Trial Data (2015) (3)
Analysis of Some Censored Survival Data From a Large-Scale Study of Melanoma (1980) (3)
An overview of skew distributions in model-based clustering (2021) (3)
Normalized Gaussian Networks with Mixed Feature Data (2005) (2)
Multivariate analysis: Classification and discriminant analysis (2001) (2)
Mixture Model-based Statistical Pattern Recognition of Clustered or Longitudinal Data (2005) (2)
Supervised Classification of Tissue Samples (2005) (2)
Statistical Evaluation of Labeled Comparative Profiling Proteomics Experiments Using Permutation Test. (2017) (2)
A comparative study of two matrix factorization methods applied to the classification of gene expression data (2010) (2)
A simple multithreaded implementation of the EM algorithm for mixture models (2016) (2)
A Block Minorization–Maximization Algorithm for Heteroscedastic Regression (2016) (2)
Clustering methods for gene-expression data (2009) (2)
Classification Of Disorders Of Anemia On The Basis Of Mixture Model Parameters (2001) (2)
On Mean And/or Variance Mixtures of Normal Distributions (2020) (2)
Clustering of Gene-Expression Profiles (2006) (2)
On formulations of skew factor models: Skew factors and/or skew errors (2021) (2)
On formulations of skew factor models: skew errors versus skew factors (2018) (2)
Robust Estimation in Gaussian Mixtures Using Multiresolution Kd-trees (2003) (2)
Professor Gopal Kanji's retirement as editor of Journal of Applied Statistics (2008) (2)
Basic Theory of the EM Algorithm (2007) (2)
Further results on discrimination with autocorrelated observations (1988) (2)
On mixture modelling with multivariate skew distributions (2016) (2)
Computation: Expectation-Maximization Algorithm (2015) (2)
Unsupervised Component-Wise EM Learning for Finite Mixtures of Skew t-distributions (2016) (2)
Microarrays in Gene Expression Studies (2005) (2)
Discrimination via Normal Models (2005) (2)
Multi-Node EM Algorithm for Finite Mixture Models (2020) (2)
Mixtures - Models and Applications (2019) (2)
Selection of Feature Variables in Discriminant Analysis (2005) (2)
A new algorithm for support vector regression with automatic selection of hyperparameters (2022) (1)
Using cluster analysis to improve gene selection in the formation of discriminant rules for the prediction of disease outcomes (2013) (1)
Classification of High-Dimensional microarray Data with a Two-Step Procedure via a Wilcoxon Criterion and Multilayer Perceptron (2011) (1)
Likelihood-based approaches to pattern recognition (1996) (1)
Skew-normal generalized spatial panel data model (2019) (1)
False discovery rate control for grouped or discretely supported p-values with application to a neuroimaging study (2019) (1)
Application of Mixture Models to Detect Differentially Expressed Genes (2005) (1)
Linear mixed models with marginally symmetric nonparametric random effects (2016) (1)
Statistical matching of non-Gaussian data (2019) (1)
Estimation of mixing proportions in the presence of autoregressively correlated training data:the case of two univariate normal populations (1994) (1)
On the mean square error associated with adaptive generalized ridge regression (1980) (1)
Estimation of Classification Rules from Partially Classified Data (2020) (1)
Assessing the Significance of Groups in High-Dimensional Data (2010) (1)
Gene expression A Mixture model with random-effects components for clustering correlated gene-expression profiles (2006) (1)
Linking gene-expression experiments with survival-time data. (2004) (1)
Likelihood‐Based Approaches to Discrimination (2005) (1)
Heterogeneity in schizophrenia: A mixture model analysis based on age-of-onset, gender and diagnosis (1998) (1)
Bayesian Approach to Mixture Analysis (2005) (1)
High Dimensional Statistics : Advances and Challenges Conference Topics : High Dimensional Analysis Survival Analysis (2011) (1)
Stream-suitable optimization algorithms for some soft-margin support vector machine variants (2018) (1)
Linking Microarray Data with Survival Analysis (2005) (1)
A Mixture of Regressions Model of COVID-19 Death Rates and Population Comorbidities (2020) (1)
CRRAO Advanced Institute of Mathematics, Statistics and Computer Science (AIMSCS) (2013) (1)
Statistical file-matching of non-Gaussian data: A game theoretic approach (2021) (1)
Time-series as an alternative analysis of seasonality in schizophrenia birth-rates (1995) (1)
Positive Data Kernel Density Estimation via the LogKDE Package for R (2018) (1)
Testing for Group Structure in High-Dimensional Data (2011) (1)
A Strategy towards the Automated Estimation of Stroke Evolution Utilising a Diffusion and Perfusion MRI based Predictive Model (2001) (1)
Mixture Models for Failure‐Time Data (2005) (1)
Variants of the EM Algorithm for Large Databases (2005) (1)
Order selection with confidence for finite mixture models (2021) (1)
Private Distributed Three-Party Learning of Gaussian Mixture Models (2017) (1)
Multivariate Analysis: Classification and Discrimination (2015) (1)
Multivariate mixture models for classification of anemias (2000) (1)
Multiple linear regression & finite mixture distribution modelling for sequential analysis of hematological data (1995) (1)
Fitting Mixture Models to Binned Data (2005) (1)
Harmless label noise and informative soft-labels in supervised classification (2021) (1)
On the identification of correlated differential features for supervised classification of high-dimensional data (2017) (1)
On finite mixtures of skew distributions (2013) (1)
Robust estimation of mixtures of Skew Normal Distributions (2016) (1)
Skew-normal Bayesian spatial heterogeneity panel data models (2019) (1)
Mixture of regression models with latent variables and sparse coefficient parameters. (2014) (1)
On the Simultaneous Use of Clinical and Microarray Expression Data in the Cluster Analysis of Tissue Samples (2004) (1)
Issues of robustness and high dimensionality in cluster analysis (2006) (1)
Multilevel modeling for inference of genetic regulatory networks (2005) (1)
Functional Mixtures-of-Experts (2022) (1)
Mixtures with Nonnormal Components (2005) (1)
Clustering of time-course gene expression profiles using normal mixture models with AR(1) random effects (2011) (1)
Faster Functional Clustering via Gaussian Mixture Models (2016) (1)
On the Gradient-based Algorithm for Matrix Factorization Applied to Dimensionality Reduction (2010) (1)
Spatial False Discovery Rate Control for Magnetic Resonance Imaging Studies (2013) (1)
Modelling Nonlinearity By Mixtures Of Factor Analyzers Via Extensions Of The Em Algorithm (2000) (1)
Mixturemodelling for cluster analysis (2004) (1)
Comment on "Hidden truncation hyperbolic distributions, finite mixtures thereof and their application for clustering" Murray, Browne, and \McNicholas (2019) (1)
Finding group structures in “Big Data” in healthcare research using mixture models (2016) (1)
Clustering of time-course gene expression profiles using normal mixture models with autoregressive random effects (2012) (1)
Screening and Clustering of Genes (2005) (1)
Application of multiple imputation to incomplete three-way three-mode multi-environment trial data (2014) (0)
Mixture Distributions – Further Developments (2016) (0)
Maximum likelihood estimation of Gaussian mixture models without matrix operations (2015) (0)
Clustering of Tissue Samples (2005) (0)
Hidden Markov Models (2005) (0)
Further Applications of the EM Algorithm (2007) (0)
Bioinformatics Research in Australia (2003) (0)
An Automated Machine learning (AutoML) approach to regression models in minerals processing with case studies of developing industrial comminution and flotation models (2022) (0)
Robust estimation for mixtures of skew data (2015) (0)
Some theoretical results regarding the polygonal distribution (2017) (0)
Extending the two-way mixture model program EMMIX to analyse incomplete data (1999) (0)
Bayesian analysis of generalized linear mixed models with spatial correlated and unrestricted skew normal errors (2021) (0)
Model-Based Clustering (2020) (0)
Modelling a Particular Philatelic Mixture Using the Mixture Likelihood Method of Clustering (1992) (0)
Mixtures of Spatial Spline Regressions (2013) (0)
Multivariate t Mixtures (2005) (0)
on the Criterion of Asymptotic Mean Square Error (1974) (0)
2 Some Alternatives to Improving the Robustness of Mixture Models (2006) (0)
On speeding up the EM algorithm in pattern recognition: A comparison of incremental and multiresolution KD -tree-based approaches (2002) (0)
review article Microarray Data analysis for Differential expression: a t utorial (2009) (0)
A mixture model analysis of valve replacement following aortic valve replacement with xenograft prostheses (1999) (0)
gmmsslm: Semi-supervised Gaussian mixture modeling with a missing data mechanism in R (2023) (0)
Mini-batch learning of exponential family finite mixture models (2020) (0)
Multi‐node Expectation–Maximization algorithm for finite mixture models (2021) (0)
Bias Associated with Maximum Likelihood Estimation of Multivariate Logistic Risk Function (1978) (0)
econstor Make Your Publications Visible . A Service of zbw (2004) (0)
Some Generalizations of the EM Algorithm (2007) (0)
Advances in robust estimation of skew normal mixtures (2017) (0)
Clustering-based approach for ranking differentially-expressed genes. (2011) (0)
Data fusion using factor analysis and low-rank matrix completion (2021) (0)
Distributional Results for Discrimination via Normal Models (2005) (0)
This is the published version: (2019) (0)
Cluster Analysis of High-Dimensional Data: A Case Study (2005) (0)
Letter to the Editor (2001) (0)
Parametric Discrimination via Nonnormal Models (2005) (0)
Subpopulations with Iron Deficiency, Liver Disease, or HFE Mutations Revealed by Statistical Mixture Modeling of Transferrin Saturation and Serum Ferritin Concentration in Asians, African Americans, Hispanics, and Whites. (2007) (0)
Editorial (1997) (0)
Submission for the degree of Doctor of Science (0)
4 Choice of Starting Values for the EM Algorithm (0)
discriminative methods Analysis of molecular profile data using generative and (2015) (0)
Log-transformed kernel density estimation for positive data (2018) (0)
Whole-Volume Clustering of Time Series Data from Zebrafish Brain Calcium Images via Mixture Model-Based Functional Data Analysis (2016) (0)
Mixture of factor analyzers for the clustering and visualization of high-dimensional data (2019) (0)
Small sample results for partial classification with the Studentized statistic W (1978) (0)
Deep Gaussian mixture models (2017) (0)
Model-Based Clustering of Functional Data via Gaussian Mixture Models (2016) (0)
Stream-suitable optimization algorithms for some soft-margin support vector machine variants (2018) (0)
Automated Gating and Dimension Reduction of High-Dimensional Cytometry Data (2021) (0)
Finite Mixture Models in Biostatistics (2017) (0)
Non-asymptotic oracle inequalities for the Lasso in high-dimensional mixture of experts (2020) (0)
A common factor-analytic model for classification (2013) (0)
Examples of the EM Algorithm (2007) (0)
Statistical Analysis of Microarray Data (2007) (0)
Mixture Analysis of Directional Data (2005) (0)
Are there time-dependent fluctuations in schizophrenia birthrates? (1995) (0)
Evaluating methods of estimating missing values for three-way three-mode multi-environment trial data (2013) (0)
Mixtures of factor analyzers with scale mixtures of fundamental skew normal distributions (2020) (0)
A Connection Between the Logit Model, Normal Discriminant-Analysis, and Multivariate Normal Mixtures - Comment (1993) (0)
Cleaning and Normalization (2005) (0)
A study of Major League Baseball salaries via a lasso-penalized mixture of linear regressions (2016) (0)
Joint frailty modeling of time-to-event data to elicit the evolution pathway of events: a generalized linear mixed model approach (2021) (0)
Monte Carlo Versions of the EM Algorithm (2007) (0)
Model-based clustering and classification with non-normal mixture distributions (2013) (0)
Some Simulation and Empirical Results for Semi-Supervised Learning of the Bayes Rule of Allocation (2022) (0)
AN l 1 -ORACLE INEQUALITY FOR THE LASSO IN HIGH-DIMENSIONAL MIXTURES OF EXPERTS MODELS (2022) (0)
Appendix: Mixture Software (2005) (0)
Assessing the Reliability of the Estimated Posterior Probabilities of Group Membership (2005) (0)
Standard Errors and Speeding up Convergence (2007) (0)
Merging Algorithm to Reduce Dimensionality in Application to Web-Mining (2007) (0)
Data Analytic Considerations with Normal Theory‐Based Discriminant Analysis (2005) (0)
Rejoinder to the discussion of “Model-based clustering and classification with non-normal mixture distributions” (2013) (0)
B ioinformatics R esearch (2003) (0)
2 LEVEL THE CLASSIFICATION AND MIXTURE MAXIMUM LIKELIHOOD APPROACHES TO CLUSTER ANALYSIS (0)
Clustering via Mixture Models with Flexible Components (2015) (0)
A spatial heterogeneity mixed model with skew-elliptical distributions (2022) (0)
The analysis of time-related events after cardiac surgery (1991) (0)
An Enduring Interest in Classification: Supervised and Unsupervised (2012) (0)
Early Detection of the Development of Iron-Deficiency by Patient-Specific Sequential-Analysis of Hematological Tests (1994) (0)
Some Practical Aspects and Variants of Normal Theory‐Based Discriminant Rules (2005) (0)
On relations between Genes and metagenes obtained via gradient-based matrix factorization (2010) (0)
Flexible Modelling via Multivariate Skew Distributions (2019) (0)
Some Cluster Analysis Methods (2005) (0)
False Discovery Rate Control Under Reduced Precision Computation (2018) (0)

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