P. McNicholas

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Analytic calculations for the EM algorithm for multivariate skew-t mixture models

Irene Vrbik, P. McNicholas, 2012 .

Multivariate sharp quadratic bounds via $\mathbf{\Sigma}$-strong convexity and the Fenchel connection

Ryan P. Browne, P. McNicholas, R. Browne, 2015 .

P. McNicholas, 2007 .

Inhibition of stearoyl-CoA desaturase-1 in differentiating 3T3-L1 preadipocytes upregulates elongase 6 and downregulates genes affecting triacylglycerol synthesis

J. C. Ralston, P. McNicholas, D. Mutch, 2014, International Journal of Obesity.

Metabolic and co-expression network-based analyses associated with nitrate response in rice

Caitlin M. A. Simopoulos, P. McNicholas, T. Zhu, 2014, BMC Genomics.

Chapter I Association Rules : An Overview

P. McNicholas, 2009 .

Using Subset Log-Likelihoods to Trim Outliers in Gaussian Mixture Models

P. McNicholas, Katharine M. Clark, 2019 .

Model-Based Clustering and Classification Using Mixtures of Multivariate Skewed Power Exponential Distributions

Ryan P. Browne, P. McNicholas, U. Dang, 2019, Journal of Classification.

Clustering Higher Order Data: Finite Mixtures of Multidimensional Arrays

Peter A. Tait, P. McNicholas, 2019, 1907.08566.

Dimension Reduction in Clustering

P. McNicholas, M. Marbac, 2016 .

Hypothesis Testing for Mixture Model Selection

Ryan P. Browne, P. McNicholas, A. Punzo, 2016 .

Handling missing data in consumer hedonic tests arising from direct scaling

Ryan P. Browne, P. McNicholas, J. C. Castura, 2016 .

Families of Parsimonious Finite Mixtures of Regression Models

P. McNicholas, U. Dang, 2013, 1312.0518.

Hypothesis Testing for Parsimonious Gaussian Mixture Models

Ryan P. Browne, P. McNicholas, A. Punzo, 2014, 1405.0377.

Mixtures of Skewed Hypercubes

Ryan P. Browne, P. McNicholas, B. Franczak, 2014 .

Nitrogen limitation and high density responses in rice suggest a role for ethylene under high density stress

P. McNicholas, M. Misyura, S. Rothstein, 2014, BMC Genomics.

A mixture of generalized hyperbolic distributions

Ryan P. Browne, P. McNicholas, R. Browne, 2013, 1305.1036.

Non-Gaussian Mixtures for Dimension Reduction, Clustering, Classification, and Discriminant Analysis

P. McNicholas, Katherine Morris, 2013 .

Mixture Model Averaging for Clustering and Classification

P. McNicholas, Yuhong Wei, 2012 .

Model‐based clustering of longitudinal data

P. McNicholas, T. B. Murphy, T. Brendan Murphy, 2010 .

Comparative analysis of chemical similarity methods for modular natural products with a hypothetical structure enumeration algorithm

P. McNicholas, M. Skinnider, C. Dejong, 2017, Journal of Cheminformatics.

A Latent Gaussian Mixture Model for Clustering Longitudinal Data

P. McNicholas, Vanessa S.E. Bierling, 2018, 1804.05133.

Mixture Model-Based Classification

P. McNicholas, 2016 .

Parsimonious Mixtures of Matrix Variate Bilinear Factor Analyzers

P. McNicholas, M. Gallaugher, 2019, Advanced Studies in Behaviormetrics and Data Science.

Promzea: a pipeline for discovery of co-regulatory motifs in maize and other plant species and its application to the anthocyanin and phlobaphene biosynthetic pathways and the Maize Development Atlas

P. McNicholas, D. Ashlock, Christophe Liseron-Monfils, 2013, BMC Plant Biology.

Predicting hospital and emergency department utilization among community-dwelling older adults: Statistical and machine learning approaches

P. McNicholas, A. Costa, Aaron Jones, 2018, PloS one.

Genome-wide expression profiling of maize in response to individual and combined water and nitrogen stresses

Sanjeena Subedi, Yong-Mei Bi, X. Chen, 2013, BMC Genomics.

Review and implementation of cure models based on first hitting times for Wiener processes

Paul D. McNicholas, Anthony F. Desmond, P. McNicholas, 2009, Lifetime data analysis.

Detecting British Columbia coastal rainfall patterns by clustering Gaussian processes

P. McNicholas, F. Paton, 2018, Environmetrics.

Clustering and semi‐supervised classification for clickstream data via mixture models

P. McNicholas, M. Gallaugher, 2018, Canadian Journal of Statistics.

Clustering gene expression time course data using mixtures of multivariate t-distributions

Paul D. McNicholas, Sanjeena Subedi, P. McNicholas, 2012 .

Finite mixtures of matrix-variate Poisson-log normal distributions for three-way count data

P. McNicholas, S. Rothstein, S. Subedi, 2018, 1807.08380.

Bayesian and likelihood inference for cure rates based on defective inverse Gaussian regression models

P. McNicholas, A. Desmond, Jeremy Balka, 2011 .

Assessing and Visualizing Matrix Variate Normality

P. McNicholas, Katharine M. Clark, M. Gallaugher, 2019, 1910.02859.

Constrained Optimization for a Subset of the Gaussian Parsimonious Clustering Models

Ryan P. Browne, P. McNicholas, S. Subedi, 2013, 1306.5824.

Editorial for the 4th Special Issue on advances in mixture models

John Hinde, Paul D. McNicholas, Tsung-I Lin, 2019, Comput. Stat. Data Anal..

Four Skewed Tensor Distributions

Paul D. McNicholas, Michael P.B. Gallaugher, Peter A. Tait, 2021, 2106.08984.

Outlier Detection via Parsimonious Mixtures of Contaminated Gaussian Distributions

P. McNicholas, A. Punzo, 2013 .

Note of Clarification on 'Hidden truncation hyperbolic distributions, finite mixtures thereof, and their application for clustering', by Murray, Browne, and McNicholas, J. Multivariate Anal. 161 (2017) 141-156

Ryan P. Browne, Paul D. McNicholas, Paula M. Murray, 2019, J. Multivar. Anal..

Clustering, classification, discriminant analysis, and dimension reduction via generalized hyperbolic mixtures

P. McNicholas, Katherine Morris, 2013, Comput. Stat. Data Anal..

Index of authors

M. Straf, P. McNicholas, M. Hudgens, 1982, Proceedings of the Nutrition Society.

A Pseudo-EM Algorithm for Clustering Incomplete Longitudinal Data

P. McNicholas, A. Desmond, M. Shaikh, 2010, The international journal of biostatistics.

Multivariate sharp quadratic bounds via -strong convexity and the Fenchel connection

Ryan P. Browne, P. McNicholas, 2016 .

Methodological Issues and the Impact of Age Stratification on the Proportion of Participants with Low Appendicular Lean Mass When Adjusting for Height and Fat Mass Using Linear Regression: Results from the Canadian Longitudinal Study on Aging

P. McNicholas, P. Raina, L. Thabane, 2020, The journal of nutrition, health & aging.

P. McNicholas, P. Raina, L. Thabane, 2020, The Journal of Frailty & Aging.

A Mixture of Coalesced Generalized Hyperbolic Distributions

Ryan P. Browne, P. McNicholas, B. Franczak, 2014, J. Classif..

Finite mixtures of matrix variate Poisson-log normal distributions for three-way count data

P. McNicholas, S. Rothstein, S. Subedi, 2018, Bioinformatics.

On nomenclature for, and the relative merits of, two formulations of skew distributions

Ryan P. Browne, P. McNicholas, M. Genton, 2014, 1402.5431.

Robust Asymmetric Clustering

Ryan P. Browne, P. McNicholas, A. Punzo, 2014 .

Model-based clustering of microarray expression data via latent Gaussian mixture models

P. McNicholas, T. B. Murphy, Paul D. McNicholas, 2010, Bioinform..

The 2nd special issue on advances in mixture models

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

A Mixture of Coalesced Generalized Hyperbolic Distributions

Ryan P. Browne, P. McNicholas, B. Franczak, 2019, Journal of Classification.

Multivariate Response and Parsimony for Gaussian Cluster-Weighted Models

Ryan P. Browne, P. McNicholas, S. Ingrassia, 2017, Journal of Classification.

Mixtures of Hidden Truncation Hyperbolic Factor Analyzers

Ryan P. Browne, P. McNicholas, P. Murray, 2019, Journal of Classification.

Variable Selection for Clustering and Classification

Jeffrey L. Andrews, P. McNicholas, J. Andrews, 2013, Journal of Classification.

A Variational Approximations-DIC Rubric for Parameter Estimation and Mixture Model Selection Within a Family Setting

P. McNicholas, Sanjeena Subedi, 2020, Journal of Classification.

Mixtures of Shifted Asymmetric Laplace Distributions

Ryan P. Browne, P. McNicholas, B. Franczak, 2012 .

On Fractionally-Supervised Classification: Weight Selection and Extension to the Multivariate t-Distribution

P. McNicholas, M. Gallaugher, 2018, Journal of Classification.

Robust Clustering in Regression Analysis via the Contaminated Gaussian Cluster-Weighted Model

P. McNicholas, A. Punzo, 2017, Journal of Classification.

Orthogonal Stiefel manifold optimization for eigen-decomposed covariance parameter estimation in mixture models

Ryan P. Browne, P. McNicholas, 2012, Statistics and Computing.

A partial EM algorithm for model‐based clustering with highly diverse missing data patterns

Ryan P. Browne, P. McNicholas, C. Findlay, 2021, Stat.

Model-Based Clustering

P. McNicholas, 2016, J. Classif..

Model-based clustering, classification, and discriminant analysis via mixtures of multivariate t-distributions

Jeffrey L. Andrews, P. McNicholas, 2011, Statistics and Computing.

Mixtures of skewed matrix variate bilinear factor analyzers

P. McNicholas, M. Gallaugher, 2019, Advances in Data Analysis and Classification.

A LASSO-penalized BIC for mixture model selection

P. McNicholas, Sakyajit Bhattacharya, 2013, Advances in Data Analysis and Classification.

Mixture model averaging for clustering

P. McNicholas, Yuhong Wei, 2014, Advances in Data Analysis and Classification.

Clustering and classification via cluster-weighted factor analyzers

P. McNicholas, S. Ingrassia, A. Punzo, 2013, Advances in Data Analysis and Classification.

Genome-wide expression profiling of maize in response to individual and combined water and nitrogen stresses

P. McNicholas, T. Zhu, S. Rothstein, 2013, BMC Genomics.

Variational Bayes approximations for clustering via mixtures of normal inverse Gaussian distributions

P. McNicholas, Sanjeena Subedi, 2014, Advances in Data Analysis and Classification.

Multivariate cluster weighted models using skewed distributions

P. McNicholas, A. Punzo, M. Gallaugher, 2021, Advances in Data Analysis and Classification.

Clustering discrete-valued time series

P. McNicholas, D. Karlis, Tyler Roick, 2020, Advances in Data Analysis and Classification.

A mixture of generalized hyperbolic factor analyzers

Ryan P. Browne, P. McNicholas, C. Tortora, 2015, Advances in Data Analysis and Classification.

A Variational Approximations-DIC Rubric for Parameter Estimation and Mixture Model Selection Within a Family Setting

Paul D. McNicholas, Sanjeena Subedi, P. McNicholas, 2013, Journal of Classification.

Two-way learning with one-way supervision for gene expression data

Paul D. McNicholas, Monica H. T. Wong, David M. Mutch, 2017, BMC Bioinformatics.

Multivariate Response and Parsimony for Gaussian Cluster-Weighted Models

Ryan P. Browne, Paul D. McNicholas, Utkarsh J. Dang, 2014, Journal of Classification.

Title Model-Based clustering of microarray expression data vialatent Gaussian mixture models

P. McNicholas, T. B. Murphy, 2017 .

Mixtures of multivariate power exponential distributions

Ryan P. Browne, P. McNicholas, U. Dang, 2015, Biometrics.

Fractionally-Supervised Classification

Paul D. McNicholas, Irene Vrbik, Irene Vrbik, 2013, Journal of Classification.

On Model-Based Clustering, Classification, and Discriminant Analysis

P. McNicholas, 2011 .

Model-based clustering of microarray expression data via latent Gaussian mixture models

Paul D. McNicholas, Thomas Brendan Murphy, P. McNicholas, 2010, Bioinform..

Mixture model averaging for clustering

Paul D. McNicholas, Yuhong Wei, P. McNicholas, 2012, Advances in Data Analysis and Classification.

A Probabilistic Distance Clustering Algorithm Using Gaussian and Student-t Multivariate Density Distributions

Paul D. McNicholas, Francesco Palumbo, Cristina Tortora, 2020, SN Computer Science.

Extending Growth Mixture Models Using Continuous Non-Elliptical Distributions

P. McNicholas, D. Steinley, Emilie M Shireman, 2017, 1703.08723.

Mixtures of skew-t factor analyzers

Ryan P. Browne, Paul D. McNicholas, Paula M. Murray, 2013, Comput. Stat. Data Anal..

Narrowband UVB and PUVA in the treatment of mycosis fungoides: a retrospective study.

P. McNicholas, S. Rogers, P. Collins, 2007, Acta dermato-venereologica.

Changes on enological parameters of white wine packaged in bag-in-box during secondary shelf life.

P. McNicholas, L. Lim, Y. Fu, 2009, Journal of food science.

Mixtures of Multiple Scaled Generalized Hyperbolic Distributions

Ryan P. Browne, Paul D. McNicholas, Cristina Tortora, 2014 .

Mixtures of Shifted AsymmetricLaplace Distributions

Ryan P. Browne, Paul D. McNicholas, Brian C. Franczak, 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Parsimonious Shifted Asymmetric Laplace Mixtures

Ryan P. Browne, P. McNicholas, P. Murray, 2013, 1311.0317.

Mixtures of Shifted Asymmetric Laplace Distributions

Ryan P. Browne, P. McNicholas, B. Franczak, 2012 .

A First Passage Time Model for Long-Term Survivors with Competing Risks

P. McNicholas, A. Desmond, G. Darlington, 2011 .

Orthogonal Stiefel manifold optimization for eigen-decomposed covariance parameter estimation in mixture models

Ryan P. Browne, Paul D. McNicholas, P. McNicholas, 2012, Statistics and Computing.

Comparing two formulations of skew distributions with special reference to model-based clustering

Ryan P. Browne, Paul D. McNicholas, Marc G. Genton, 2014 .

Using evolutionary algorithms for model-based clustering

Paul D. McNicholas, Jeffrey L. Andrews, P. McNicholas, 2013, Pattern Recognit. Lett..

Model-based clustering, classification, and discriminant analysis via mixtures of multivariate t-distributions

Paul D. McNicholas, Jeffrey L. Andrews, P. McNicholas, 2011, Statistics and Computing.

Serial and parallel implementations of model-based clustering via parsimonious Gaussian mixture models

Paul D. McNicholas, Thomas Brendan Murphy, Aaron F. McDaid, 2010, Comput. Stat. Data Anal..

Variable Selection for Clustering and Classification

Paul D. McNicholas, Jeffrey L. Andrews, P. McNicholas, 2013, J. Classif..

Caloric Restriction Induces Changes in Insulin and Body Weight Measurements That Are Inversely Associated with Subsequent Weight Regain

A. Astrup, J. Martínez, P. McNicholas, 2012, PloS one.

A multivariate Poisson-log normal mixture model for clustering transcriptome sequencing data

Paul D. McNicholas, Sanjeena Subedi, Steven J. Rothstein, 2017, BMC Bioinformatics.

Reliability of transcranial magnetic stimulation measures of afferent inhibition

P. McNicholas, L. Beaulieu, Claudia V. Turco, 2019, Brain Research.

The prevalence of sarcopenia in community-dwelling older adults, an exploration of differences between studies and within definitions: a systematic review and meta-analyses

P. McNicholas, P. Raina, P. Raina, 2018, Age and ageing.

Estimating common principal components in high dimensions

Ryan P. Browne, Paul D. McNicholas, P. McNicholas, 2013, Advances in Data Analysis and Classification.

Accelerated Failure Time Models for Competing Risks in a Cluster Weighted Modelling Framework

P. McNicholas, U. Dang, 2013, 1312.0859.

ContaminatedMixt: An R Package for Fitting Parsimonious Mixtures of Multivariate Contaminated Normal Distributions

Paul D. McNicholas, Antonio Punzo, Angelo Mazza, 2016, 1606.03766.

Clustering with the multivariate normal inverse Gaussian distribution

Paul D. McNicholas, Dimitris Karlis, Thomas Brendan Murphy, 2016, Comput. Stat. Data Anal..

Unsupervised learning via mixtures of skewed distributions with hypercube contours

Ryan P. Browne, Paul D. McNicholas, Cristina Tortora, 2014, Pattern Recognit. Lett..

Do Different Ascertainment Techniques Identify the Same Individuals as Sarcopenic in the Canadian Longitudinal Study on Aging?

P. McNicholas, P. Raina, L. Thabane, 2020, Journal of the American Geriatrics Society.

The impact of different diagnostic criteria on the association of sarcopenia with injurious falls in the CLSA

P. McNicholas, P. Raina, L. Thabane, 2020, Journal of cachexia, sarcopenia and muscle.

Muscle Androgen Receptor Content but Not Systemic Hormones Is Associated With Resistance Training-Induced Skeletal Muscle Hypertrophy in Healthy, Young Men

P. McNicholas, Stuart M Phillips, S. Fujita, 2018, Front. Physiol..

Robust Clustering in Regression Analysis via the Contaminated Gaussian Cluster-Weighted Model

Paul D. McNicholas, Antonio Punzo, P. McNicholas, 2014, J. Classif..

Model-Based Clustering for Sensory Data and Liking

Ryan P. Browne, Paul McNicholas, Ryan Browne, 2014 .

Modelling receiver operating characteristic curves using Gaussian mixtures

Paul D. McNicholas, Amay S. M. Cheam, P. McNicholas, 2014, Comput. Stat. Data Anal..

Mixtures of generalized hyperbolic distributions and mixtures of skew-t distributions for model-based clustering with incomplete data

Yang Tang, Paul D. McNicholas, Yuhong Wei, 2017, Comput. Stat. Data Anal..

Parsimonious mixtures of multivariate contaminated normal distributions

P. McNicholas, A. Punzo, 2013, Biometrical journal. Biometrische Zeitschrift.

A LASSO-penalized BIC for mixture model selection

Paul D. McNicholas, Sakyajit Bhattacharya, P. McNicholas, 2012, Advances in Data Analysis and Classification.

Matrix Normal Cluster-Weighted Models

Paul D. McNicholas, Antonio Punzo, Salvatore D. Tomarchio, 2021, Journal of Classification.

Cluster-weighted $$t$$t-factor analyzers for robust model-based clustering and dimension reduction

Paul D. McNicholas, Salvatore Ingrassia, Antonio Punzo, 2015, Stat. Methods Appl..

Clustering and classification via cluster-weighted factor analyzers

Paul D. McNicholas, Salvatore Ingrassia, Antonio Punzo, 2012, Advances in Data Analysis and Classification.

Parsimonious Gaussian mixture models

Paul D. McNicholas, Thomas Brendan Murphy, P. McNicholas, 2008, Stat. Comput..

teigen: An R Package for Model-Based Clustering and Classification via the Multivariate t Distribution

Paul D. McNicholas, Jeffrey L. Andrews, Nicholas M. Boers, 2018 .

Robust Model-Based Clustering of Voting Records

P. McNicholas, A. Punzo, Yang Tang, 2018, 1805.04203.

Flexible clustering of high‐dimensional data via mixtures of joint generalized hyperbolic distributions

Ryan P. Browne, Paul D. McNicholas, Yang Tang, 2017, 1705.03130.

Hidden truncation hyperbolic distributions, finite mixtures thereof, and their application for clustering

Ryan P. Browne, Paul D. McNicholas, Paula M. Murray, 2017, J. Multivar. Anal..

Model‐based clustering for spatiotemporal data on air quality monitoring

Paul D. McNicholas, A. S. M. Cheam, M. Marbac, 2017 .

Identification of five important genes to predict glioblastoma subtypes

P. McNicholas, J. Moffat, M. Qazi, 2021, Neuro-oncology advances.

Finding Outliers in Gaussian Model-Based Clustering

P. McNicholas, Katharine M. Clark, 2019, 1907.01136.

Standardising the lift of an association rule

Paul D. McNicholas, Thomas Brendan Murphy, M. O'Regan, 2008, Comput. Stat. Data Anal..

Association Rules: An Overview

Yanchang Zhao, Paul D. McNicholas, P. McNicholas, 2009 .

Model-Based Classification via Mixtures of Multivariate t-Factor Analyzers

Michelle A. Steane, Paul D. McNicholas, Rickey Y. Yada, 2012, Commun. Stat. Simul. Comput..

Promzea: a pipeline for discovery of co-regulatory motifs in maize and other plant species and its application to the anthocyanin and phlobaphene biosynthetic pathways and the Maize Development Atlas

P. McNicholas, D. Ashlock, Christophe Liseron-Monfils, 2013, BMC Plant Biology.

Standardizing interestingness measures for association rules pdf

Paul D. McNicholas, Thomas Brendan Murphy, Mateen Shaikh, 2013, Stat. Anal. Data Min..

The LASSO and Sparse Least Squares Regression Methods for SNP Selection in Predicting Quantitative Traits

Xiaojian Yang, Paul D. McNicholas, Sanjeena Subedi, 2012, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

Model-Based Learning Using a Mixture of Mixtures of Gaussian and Uniform Distributions

Ryan P. Browne, Paul D. McNicholas, Matthew D. Sparling, 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Model-Based Clustering and Classification for Longitudinal Data

Paul D. McNicholas, Sanjeena Subedi, K. Raju Jampani, 2015 .

A mixture of generalized hyperbolic factor analyzers

Ryan P. Browne, Paul D. McNicholas, Cristina Tortora, 2013, Advances in Data Analysis and Classification.

A mixture of SDB skew-t factor analyzers

Ryan P. Browne, Paul D. McNicholas, Paula M. Murray, 2013, 1310.6224.

Capturing patterns via parsimonious t mixture models

Paul D. McNicholas, Tsung-I Lin, Hsiu J. Ho, 2013, 1303.2316.

Parsimonious skew mixture models for model-based clustering and classification

Paul D. McNicholas, Irene Vrbik, Irene Vrbik, 2013, Comput. Stat. Data Anal..

Extending mixtures of multivariate t-factor analyzers

P. McNicholas, J. Andrews, 2011, Stat. Comput..

Mixtures of modified t-factor analyzers for model-based clustering, classification, and discriminant

P. McNicholas, J. Andrews, 2011 .

Model-based classification via mixtures of multivariate t-distributions

Paul D. McNicholas, Jeffrey L. Andrews, Sanjeena Subedi, 2011, Comput. Stat. Data Anal..

Defying the Circadian Rhythm: Clustering Participant Telemetry in the UK Biobank Data

Paul D. McNicholas, Mark Farrell, Nikola Pocuca, 2020, 2011.08350.

Trajectories of Symptom Severity in Children with Autism: Variability and Turning Points through the Transition to School

Peter A. Tait, Stephen J. Gentles, L. Zwaigenbaum, 2021, Journal of autism and developmental disorders.

Subspace clustering with the multivariate-t distribution

Paul D. McNicholas, Brian C. Franczak, Angelina Pesevski, 2017, Pattern Recognit. Lett..

Vaccenic acid in serum triglycerides is associated with markers of insulin resistance in men.

P. McNicholas, D. Mutch, A. Duncan, 2012, Applied physiology, nutrition, and metabolism = Physiologie appliquee, nutrition et metabolisme.

Re-engaging in Aging and Mobility Research in the COVID-19 Era: Early Lessons from Pivoting a Large-Scale, Interdisciplinary Study amidst a Pandemic

P. McNicholas, K. Newbold, J. Richardson, 2021, Canadian Journal on Aging / La Revue canadienne du vieillissement.

Multivariate cluster weighted models using skewed distributions

Paul D. McNicholas, Antonio Punzo, Michael P.B. Gallaugher, 2021, Adv. Data Anal. Classif..

Mixtures of Hidden Truncation Hyperbolic Factor Analyzers

Ryan P. Browne, Paul D. McNicholas, Paula M. Murray, 2017, J. Classif..

Model-Based Clustering

Paul D. McNicholas, P. McNicholas, 2016, Journal of Classification.

Model-Based Clustering Using Mixtures of Coalesced Generalized Hyperbolic Distributions

Ryan P. Browne, Paul D. McNicholas, Cristina Tortora, 2014 .

Asymmetric clusters and outliers: Mixtures of multivariate contaminated shifted asymmetric Laplace distributions

Ryan P. Browne, Paul D. McNicholas, Antonio Punzo, 2014, Comput. Stat. Data Anal..

Mixtures of Variance-Gamma Distributions

Ryan P. Browne, Paul D. McNicholas, Sharon M. McNicholas, 2013, 1309.2695.

Variational Bayes approximations for clustering via mixtures of normal inverse Gaussian distributions

Paul D. McNicholas, Sanjeena Subedi, P. McNicholas, 2013, Advances in Data Analysis and Classification.

A mixture of common skew‐t factor analysers

Ryan P. Browne, Paul D. McNicholas, Paula M. Murray, 2013, 1307.5558.

M E ] 2 7 O ct 2 01 8 Mixtures of Hidden Truncation Hyperbolic Factor Analyzers

P. McNicholas, P. Murray, R. Browne, 2018 .

A Mixture of Variance-Gamma Factor Analyzers

P. McNicholas, S. McNicholas, R. Browne, 2017 .

Model-Based Clustering, Classification, and Discriminant Analysis Using the Generalized Hyperbolic Distribution: MixGHD R package

Ryan P. Browne, Paul D. McNicholas, Cristina Tortora, 2021, J. Stat. Softw..

An Evolutionary Algorithm with Crossover and Mutation for Model-Based Clustering

Paul D. McNicholas, Daniel A. Ashlock, Sharon M. McNicholas, 2018, Journal of Classification.

Flexible High-Dimensional Unsupervised Learning with Missing Data

Yang Tang, Yuhong Wei, Paul David McNicholas, 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

A matrix variate skew‐t distribution

P. McNicholas, M. Gallaugher, 2017, Pattern Recognit..

High-dimensional unsupervised classification via parsimonious contaminated mixtures

Paul D. McNicholas, Antonio Punzo, Martin Blostein, 2014, Pattern Recognit..

Robust High-Dimensional Modeling with the Contaminated Gaussian Distribution

P. McNicholas, A. Punzo, 2014 .

Dimension reduction for model-based clustering via mixtures of multivariate $$t$$t-distributions

Paul D. McNicholas, Luca Scrucca, Katherine Morris, 2013, Adv. Data Anal. Classif..

Model-based clustering, classification, and discriminant analysis of data with mixed type

Ryan P. Browne, Paul D. McNicholas, P. McNicholas, 2012 .

Mixtures of Matrix-Variate Contaminated Normal Distributions

P. McNicholas, A. Punzo, M. Gallaugher, 2021, J. Comput. Graph. Stat..

Julia and R Packages Used Herein

Paul D. McNicholas, Peter A. Tait, P. McNicholas, 2019, Data Science with Julia.

Discussion of ‘Model-based clustering and classification with non-normal mixture distributions’ by Lee and McLachlan

P. McNicholas, P. Murray, R. Browne, 2013, Statistical Methods & Applications.

Discussion of ‘Model-based clustering and classification with non-normal mixture distributions’ by Lee and McLachlan

Ryan P. Browne, Paul D. McNicholas, Paula M. Murray, 2013, Stat. Methods Appl..

Clustering Airbnb Reviews

Yang Tang, Paul D. McNicholas, P. McNicholas, 2017, 1705.03134.

Model based clustering of high-dimensional binary data

Yang Tang, Ryan P. Browne, Paul D. McNicholas, 2014, Comput. Stat. Data Anal..

Model-based classification using latent Gaussian mixture models

P. McNicholas, 2010 .

Skewed Distributions or Transformations? Modelling Skewness for a Cluster Analysis

Paul D. McNicholas, Volodymyr Melnykov, Michael P. B. Gallaugher, 2020, 2011.09152.

Two-way learning with one-way supervision for gene expression data

P. McNicholas, D. Mutch, M. H. Wong, 2017, BMC Bioinformatics.

Mixtures of skewed matrix variate bilinear factor analyzers

Paul D. McNicholas, Michael P.B. Gallaugher, P. McNicholas, 2018, Advances in Data Analysis and Classification.

A Mixture of Matrix Variate Bilinear Factor Analyzers

P. McNicholas, M. Gallaugher, 2017, 1712.08664.

Parameter-wise co-clustering for high-dimensional data

Christophe Biernacki, Michael Gallaugher, Paul Mcnicholas, 2019, Computational Statistics.

Relaxing the Identically Distributed Assumption in Gaussian Co-Clustering for High Dimensional Data pdf

Paul D. McNicholas, Christophe Biernacki, Michael P. B. Gallaugher, 2018, ArXiv.

An Adaptive LASSO-Penalized BIC

Paul D. McNicholas, Sakyajit Bhattacharya, P. McNicholas, 2014, 1406.1332.

Mixtures of 'Unrestricted' Skew-t Factor Analyzers

Ryan P. Browne, Paul D. McNicholas, Paula M. Murray, 2013 .

A Partial EM Algorithm for Clustering White Breads

P. McNicholas, C. Findlay, R. Browne, 2013, 1302.6625.

On Clustering and Classification Via Mixtures of Multivariate t-Distributions

Paul D. McNicholas, P. McNicholas, 2013, Statistical Models for Data Analysis.

Flexible Clustering with a Sparse Mixture of Generalized Hyperbolic Distributions

Paul D. McNicholas, Yang Tang, Michael P. B. Gallaugher, 2019, 1903.05054.

Clustering Three-Way Data with Outliers

P. McNicholas, Katharine M. Clark, 2023, ArXiv.

Product selection for liking studies: The sensory informed design

Ryan P. Browne, Paul D. McNicholas, Christopher J. Findlay, 2015 .

Estimating common principal components in high dimensions

P. McNicholas, R. Browne, 2013, Advances in Data Analysis and Classification.

Clustering, classification, discriminant analysis, and dimension reduction via generalized hyperbolic mixtures

Paul D. McNicholas, Katherine Morris, P. McNicholas, 2013, Comput. Stat. Data Anal..