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ORCIDPaul D. McNicholas
Paul David McNicholas
Person information
- affiliation: McMaster University, Department of Mathematics and Statistics, Hamilton, Canada
- affiliation: University of Guelph, Canada
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2020 – today
- 2024
[j84]Paul D. McNicholas:
Editorial: Journal of Classification Vol. 41-1. J. Classif. 41(1): 1 (2024)- [j83]Paul D. McNicholas:
Editorial: Journal of Classification Vol. 41-2. J. Classif. 41(2): 215 (2024) - [j82]Katharine M. Clark
, Paul D. McNicholas:
Finding Outliers in Gaussian Model-based Clustering. J. Classif. 41(2): 313-337 (2024) - [j81]Mark de Rooij, Brian C. Franczak, Cinzia Viroli, Arthur White, Paul D. McNicholas:
Editorial: Journal of Classification Vol. 41-3. J. Classif. 41(3): 427-428 (2024) - [j80]Mackenzie R. Neal, Alexa A. Sochaniwsky, Paul D. McNicholas:
Hidden Markov models for multivariate panel data. Stat. Comput. 34(6): 182 (2024) - [i4]Katharine M. Clark, Paul D. McNicholas:
An EM Gradient Algorithm for Mixture Models with Components Derived from the Manly Transformation. CoRR abs/2410.00848 (2024) - 2023
- [j79]Anjali Silva, Xiaoke Qin, Steven J. Rothstein, Paul D. McNicholas, Sanjeena Subedi:
Finite mixtures of matrix variate Poisson-log normal distributions for three-way count data. Bioinform. 39(5) (2023) - [j78]Paul D. McNicholas:
Editorial: Journal of Classification Vol. 40-1. J. Classif. 40(1): 1 (2023) - [j77]Utkarsh J. Dang, Michael P. B. Gallaugher, Ryan P. Browne, Paul D. McNicholas:
Model-Based Clustering and Classification Using Mixtures of Multivariate Skewed Power Exponential Distributions. J. Classif. 40(1): 145-167 (2023) - [j76]Paul D. McNicholas:
Editorial: Journal of Classification Vol. 40-2. J. Classif. 40(2): 213 (2023) - [j75]Paul D. McNicholas:
Editorial: Journal of Classification Vol. 40-3. J. Classif. 40(3): 467 (2023) - [j74]Michael P. B. Gallaugher, Christophe Biernacki, Paul D. McNicholas:
Parameter-wise co-clustering for high-dimensional data. Comput. Stat. 38(3): 1597-1619 (2023) - [i3]Katharine M. Clark, Paul D. McNicholas:
Clustering Three-Way Data with Outliers. CoRR abs/2310.05288 (2023) - 2022
- [j73]Michael P. B. Gallaugher, Salvatore D. Tomarchio, Paul D. McNicholas, Antonio Punzo:
Multivariate cluster weighted models using skewed distributions. Adv. Data Anal. Classif. 16(1): 93-124 (2022) - [j72]Michael P. B. Gallaugher, Salvatore D. Tomarchio, Paul D. McNicholas, Antonio Punzo:
Correction to: Multivariate cluster weighted models using skewed distributions. Adv. Data Anal. Classif. 16(4): 1097 (2022) - [j71]Paul D. McNicholas:
Editorial: Journal of Classification Vol. 39-1. J. Classif. 39(1): 1-2 (2022) - [j70]Paul D. McNicholas:
Editorial: Journal of Classification Vol. 39-2. J. Classif. 39(2): 217 (2022) - [j69]Paul D. McNicholas:
Editorial: Journal of Classification Vol. 39-3. J. Classif. 39(3): 409 (2022) - [j68]Salvatore D. Tomarchio, Michael P. B. Gallaugher, Antonio Punzo, Paul D. McNicholas:
Mixtures of Matrix-Variate Contaminated Normal Distributions. J. Comput. Graph. Stat. 31(1): 413-421 (2022) - 2021
- [j67]Tyler Roick, Dimitris Karlis, Paul D. McNicholas:
Clustering discrete-valued time series. Adv. Data Anal. Classif. 15(1): 209-229 (2021) - [j66]Paul D. McNicholas:
Editorial: Journal of Classification Vol. 38-1. J. Classif. 38(1): 1 (2021) - [j65]Sanjeena Subedi, Paul D. McNicholas:
A Variational Approximations-DIC Rubric for Parameter Estimation and Mixture Model Selection Within a Family Setting. J. Classif. 38(1): 89-108 (2021) - [j64]Paul D. McNicholas:
Editorial: Journal of Classification Vol. 38-2. J. Classif. 38(2): 187 (2021) - [j63]Sharon M. McNicholas, Paul D. McNicholas, Daniel A. Ashlock:
An Evolutionary Algorithm with Crossover and Mutation for Model-Based Clustering. J. Classif. 38(2): 264-279 (2021) - [j62]Paul D. McNicholas:
Editorial: Journal of Classification Vol. 38-3. J. Classif. 38(3): 423-424 (2021) - [j61]Salvatore D. Tomarchio, Paul D. McNicholas, Antonio Punzo:
Matrix Normal Cluster-Weighted Models. J. Classif. 38(3): 556-575 (2021) - [j60]Cristina Tortora, Ryan P. Browne, Aisha Elsherbiny, Brian C. Franczak, Paul D. McNicholas:
Model-Based Clustering, Classification, and Discriminant Analysis Using the Generalized Hyperbolic Distribution: MixGHD R package. J. Stat. Softw. 98(1) (2021) - 2020
- [j59]Michael P. B. Gallaugher, Paul D. McNicholas:
Mixtures of skewed matrix variate bilinear factor analyzers. Adv. Data Anal. Classif. 14(2): 415-434 (2020) - [j58]Paul D. McNicholas, Douglas L. Steinley:
Editorial: Journal of Classification Vol. 37-2. J. Classif. 37(2): 275-276 (2020) - [j57]Paula M. Murray, Ryan P. Browne, Paul D. McNicholas:
Mixtures of Hidden Truncation Hyperbolic Factor Analyzers. J. Classif. 37(2): 366-379 (2020) - [j56]Paul D. McNicholas:
Editorial: Journal of Classification Vol. 37-3. J. Classif. 37(3): 549 (2020) - [j55]Yuhong Wei, Yang Tang, Paul D. McNicholas:
Flexible High-Dimensional Unsupervised Learning with Missing Data. IEEE Trans. Pattern Anal. Mach. Intell. 42(3): 610-621 (2020) - [j54]Antonio Punzo, Martin Blostein, Paul D. McNicholas:
High-dimensional unsupervised classification via parsimonious contaminated mixtures. Pattern Recognit. 98 (2020) - [j53]Cristina Tortora, Paul D. McNicholas, Francesco Palumbo:
A Probabilistic Distance Clustering Algorithm Using Gaussian and Student-t Multivariate Density Distributions. SN Comput. Sci. 1(1): 65 (2020)
2010 – 2019
- 2019
- [j52]Anjali Silva, Steven J. Rothstein, Paul D. McNicholas, Sanjeena Subedi:
A multivariate Poisson-log normal mixture model for clustering transcriptome sequencing data. BMC Bioinform. 20(1): 394:1-394:11 (2019) - [j51]Cristina Tortora, Brian C. Franczak, Ryan P. Browne, Paul D. McNicholas:
A Mixture of Coalesced Generalized Hyperbolic Distributions. J. Classif. 36(1): 26-57 (2019) - [j50]Michael P. B. Gallaugher, Paul D. McNicholas:
On Fractionally-Supervised Classification: Weight Selection and Extension to the Multivariate t-Distribution. J. Classif. 36(2): 232-265 (2019) - [j49]Yuhong Wei, Yang Tang, Paul D. McNicholas:
Mixtures of generalized hyperbolic distributions and mixtures of skew-t distributions for model-based clustering with incomplete data. Comput. Stat. Data Anal. 130: 18-41 (2019) - [j48]Jochen Einbeck, John Hinde, Salvatore Ingrassia, Tsung-I Lin, Paul D. McNicholas:
Editorial for the 4th Special Issue on advances in mixture models. Comput. Stat. Data Anal. 132: 143-144 (2019) - [j47]Katherine Morris, Antonio Punzo, Paul D. McNicholas, Ryan P. Browne:
Asymmetric clusters and outliers: Mixtures of multivariate contaminated shifted asymmetric Laplace distributions. Comput. Stat. Data Anal. 132: 145-166 (2019) - [j46]Paula M. Murray, Ryan P. Browne, Paul D. McNicholas:
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. J. Multivar. Anal. 171: 475-476 (2019) - 2018
- [j45]Hans A. Kestler, Paul D. McNicholas, Adalbert F. X. Wilhelm:
Special issue on "Science of big data: theory, methods and applications" - Preface by the Guest Editors. Adv. Data Anal. Classif. 12(4): 823-825 (2018) - [j44]Michael P. B. Gallaugher, Paul D. McNicholas:
Finite mixtures of skewed matrix variate distributions. Pattern Recognit. 80: 83-93 (2018) - [j43]Angelina Pesevski, Brian C. Franczak, Paul D. McNicholas:
Subspace clustering with the multivariate-t distribution. Pattern Recognit. Lett. 112: 297-302 (2018) - [j42]Mateen Shaikh, Paul D. McNicholas, Maria-Luiza Antonie, Thomas Brendan Murphy:
Standardizing interestingness measures for association rules. Stat. Anal. Data Min. 11(6): 282-295 (2018) - [i2]Michael P. B. Gallaugher, Christophe Biernacki, Paul D. McNicholas:
Relaxing the Identically Distributed Assumption in Gaussian Co-Clustering for High Dimensional Data. CoRR abs/1808.08366 (2018) - 2017
- [j41]Monica H. T. Wong, David M. Mutch, Paul D. McNicholas:
Two-way learning with one-way supervision for gene expression data. BMC Bioinform. 18(1): 150:1-150:13 (2017) - [j40]Utkarsh J. Dang, Antonio Punzo, Paul D. McNicholas, Salvatore Ingrassia, Ryan P. Browne:
Multivariate Response and Parsimony for Gaussian Cluster-Weighted Models. J. Classif. 34(1): 4-34 (2017) - [j39]Antonio Punzo, Paul D. McNicholas:
Robust Clustering in Regression Analysis via the Contaminated Gaussian Cluster-Weighted Model. J. Classif. 34(2): 249-293 (2017) - [j38]Michael A. Skinnider, Chris A. Dejong, Brian C. Franczak, Paul D. McNicholas, Nathan A. Magarvey:
Comparative analysis of chemical similarity methods for modular natural products with a hypothetical structure enumeration algorithm. J. Cheminformatics 9(1): 46 (2017) - [j37]Paula M. Murray, Ryan P. Browne, Paul D. McNicholas:
Hidden truncation hyperbolic distributions, finite mixtures thereof, and their application for clustering. J. Multivar. Anal. 161: 141-156 (2017) - 2016
- [j36]Cristina Tortora, Paul D. McNicholas, Ryan P. Browne:
A mixture of generalized hyperbolic factor analyzers. Adv. Data Anal. Classif. 10(4): 423-440 (2016) - [j35]Paul D. McNicholas:
Model-Based Clustering. J. Classif. 33(3): 331-373 (2016) - [j34]John Hinde, Salvatore Ingrassia, Tsung-I Lin, Paul D. McNicholas:
The Third Special Issue on Advances in Mixture Models. Comput. Stat. Data Anal. 93: 2-4 (2016) - [j33]Adrian O'Hagan, Thomas Brendan Murphy, Isobel Claire Gormley, Paul D. McNicholas, Dimitris Karlis:
Clustering with the multivariate normal inverse Gaussian distribution. Comput. Stat. Data Anal. 93: 18-30 (2016) - [j32]Amay S. M. Cheam, Paul D. McNicholas:
Modelling receiver operating characteristic curves using Gaussian mixtures. Comput. Stat. Data Anal. 93: 192-208 (2016) - [j31]Katherine Morris, Paul D. McNicholas:
Clustering, classification, discriminant analysis, and dimension reduction via generalized hyperbolic mixtures. Comput. Stat. Data Anal. 97: 133-150 (2016) - 2015
- [j30]Yuhong Wei, Paul D. McNicholas:
Mixture model averaging for clustering. Adv. Data Anal. Classif. 9(2): 197-217 (2015) - [j29]Irene Vrbik, Paul D. McNicholas:
Fractionally-Supervised Classification. J. Classif. 32(3): 359-381 (2015) - [j28]Yang Tang, Ryan P. Browne, Paul D. McNicholas:
Model based clustering of high-dimensional binary data. Comput. Stat. Data Anal. 87: 84-101 (2015) - [j27]Brian C. Franczak, Cristina Tortora, Ryan P. Browne, Paul D. McNicholas:
Unsupervised learning via mixtures of skewed distributions with hypercube contours. Pattern Recognit. Lett. 58: 69-76 (2015) - [j26]Brian C. Franczak, Cristina Tortora, Ryan P. Browne, Paul D. McNicholas:
Corrigendum to "Unsupervised learning via mixtures of skewed distributions with hypercube contours" [Pattern Recognition Letters. 58(1), 69-76]. Pattern Recognit. Lett. 62: 68 (2015) - [j25]Sanjeena Subedi, Antonio Punzo, Salvatore Ingrassia, Paul D. McNicholas:
Cluster-weighted t-factor analyzers for robust model-based clustering and dimension reduction. Stat. Methods Appl. 24(4): 623-649 (2015) - 2014
- [j24]Sakyajit Bhattacharya, Paul D. McNicholas:
A LASSO-penalized BIC for mixture model selection. Adv. Data Anal. Classif. 8(1): 45-61 (2014) - [j23]Sanjeena Subedi, Paul D. McNicholas:
Variational Bayes approximations for clustering via mixtures of normal inverse Gaussian distributions. Adv. Data Anal. Classif. 8(2): 167-193 (2014) - [j22]Ryan P. Browne, Paul D. McNicholas:
Estimating common principal components in high dimensions. Adv. Data Anal. Classif. 8(2): 217-226 (2014) - [j21]Jeffrey L. Andrews, Paul D. McNicholas:
Variable Selection for Clustering and Classification. J. Classif. 31(2): 136-153 (2014) - [j20]Dankmar Böhning, Christian Hennig, Geoffrey J. McLachlan, Paul D. McNicholas:
The 2nd special issue on advances in mixture models. Comput. Stat. Data Anal. 71: 1-2 (2014) - [j19]Irene Vrbik, Paul D. McNicholas:
Parsimonious skew mixture models for model-based clustering and classification. Comput. Stat. Data Anal. 71: 196-210 (2014) - [j18]Paula M. Murray, Ryan P. Browne, Paul D. McNicholas:
Mixtures of skew-t factor analyzers. Comput. Stat. Data Anal. 77: 326-335 (2014) - [j17]Yu Xia, Paul D. McNicholas:
A gradient method for the monotone fused least absolute shrinkage and selection operator. Optim. Methods Softw. 29(3): 463-483 (2014) - [j16]Brian C. Franczak, Ryan P. Browne, Paul D. McNicholas:
Mixtures of Shifted AsymmetricLaplace Distributions. IEEE Trans. Pattern Anal. Mach. Intell. 36(6): 1149-1157 (2014) - [j15]Ryan P. Browne, Paul D. McNicholas:
Orthogonal Stiefel manifold optimization for eigen-decomposed covariance parameter estimation in mixture models. Stat. Comput. 24(2): 203-210 (2014) - 2013
- [j14]Sanjeena Subedi, Antonio Punzo, Salvatore Ingrassia, Paul D. McNicholas:
Clustering and classification via cluster-weighted factor analyzers. Adv. Data Anal. Classif. 7(1): 5-40 (2013) - [j13]Katherine Morris, Paul D. McNicholas, Luca Scrucca:
Dimension reduction for model-based clustering via mixtures of multivariate $$t$$ t -distributions. Adv. Data Anal. Classif. 7(3): 321-338 (2013) - [j12]Jeffrey L. Andrews, Paul D. McNicholas:
Using evolutionary algorithms for model-based clustering. Pattern Recognit. Lett. 34(9): 987-992 (2013) - [j11]Paul D. McNicholas, Ryan P. Browne, Paula M. Murray:
Discussion of 'Model-based clustering and classification with non-normal mixture distributions' by Lee and McLachlan. Stat. Methods Appl. 22(4): 467-472 (2013) - [p1]Paul D. McNicholas:
On Clustering and Classification Via Mixtures of Multivariate t-Distributions. Statistical Models for Data Analysis 2013: 233-240 - [i1]Mateen Shaikh, Paul David McNicholas, Maria-Luiza Antonie, Thomas Brendan Murphy:
Standardizing Interestingness Measures for Association Rules. CoRR abs/1308.3740 (2013) - 2012
- [j10]Michelle A. Steane, Paul D. McNicholas, Rickey Y. Yada:
Model-Based Classification via Mixtures of Multivariate t-Factor Analyzers. Commun. Stat. Simul. Comput. 41(4): 510-523 (2012) - [j9]Ryan P. Browne, Paul D. McNicholas, Matthew D. Sparling:
Model-Based Learning Using a Mixture of Mixtures of Gaussian and Uniform Distributions. IEEE Trans. Pattern Anal. Mach. Intell. 34(4): 814-817 (2012) - [j8]Jeffrey L. Andrews, Paul D. McNicholas:
Model-based clustering, classification, and discriminant analysis via mixtures of multivariate t-distributions - The tEIGEN family. Stat. Comput. 22(5): 1021-1029 (2012) - [j7]Zeny Z. Feng, Xiaojian Yang, Sanjeena Subedi, Paul D. McNicholas:
The LASSO and Sparse Least Squares Regression Methods for SNP Selection in Predicting Quantitative Traits. IEEE ACM Trans. Comput. Biol. Bioinform. 9(2): 629-636 (2012) - 2011
- [j6]Jeffrey L. Andrews, Paul D. McNicholas, Sanjeena Subedi:
Model-based classification via mixtures of multivariate t-distributions. Comput. Stat. Data Anal. 55(1): 520-529 (2011) - [j5]Jeffrey L. Andrews, Paul D. McNicholas:
Extending mixtures of multivariate t-factor analyzers. Stat. Comput. 21(3): 361-373 (2011) - [c1]Daniel A. Ashlock, Justin Schonfeld, Paul D. McNicholas:
Translation tables: A genetic code in a evolutionary algorithm. IEEE Congress on Evolutionary Computation 2011: 2685-2692 - 2010
- [j4]Paul David McNicholas, Thomas Brendan Murphy:
Model-based clustering of microarray expression data via latent Gaussian mixture models. Bioinform. 26(21): 2705-2712 (2010) - [j3]Paul David McNicholas, Thomas Brendan Murphy, Aaron F. McDaid, Dermot Frost:
Serial and parallel implementations of model-based clustering via parsimonious Gaussian mixture models. Comput. Stat. Data Anal. 54(3): 711-723 (2010)
2000 – 2009
- 2008
- [j2]Paul David McNicholas, Thomas Brendan Murphy, M. O'Regan:
Standardising the lift of an association rule. Comput. Stat. Data Anal. 52(10): 4712-4721 (2008) - [j1]Paul David McNicholas, Thomas Brendan Murphy:
Parsimonious Gaussian mixture models. Stat. Comput. 18(3): 285-296 (2008)
Coauthor Index
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