A list of presentations is given on a separate page. To export citations, see Google Scholar or ORCID.

**Monographs**McNicholas, P.D. and Tait, P.A. (2019),

McNicholas, P.D. (2016), *Mixture Model-Based Classification*. Boca Raton: Chapman & Hall/CRC Press. [webpage]

**Selected Recent Preprints**

Clark, K.M. and McNicholas, P.D. (2019), 'Using subset log-likelihoods to trim outliers in Gaussian mixture models', arXiv preprint arXiv:1907.01136v2.

Tait, P.A. and McNicholas, P.D. (2019), 'Clustering higher order data: Finite mixtures of multidimensional arrays', arXiv preprint arXiv:1907.08566.

Dang, U.J., Gallaugher, M.P.B., Browne, R.P. and McNicholas, P.D. (2019), 'Model-based clustering and classification using mixtures of multivariate skewed power exponential distributions', arXiv preprint arXiv:1907.01938.

Gallaugher, M.P.B., Tang, Y. and McNicholas, P.D. (2019), 'Flexible clustering with a sparse mixture of generalized hyperbolic distributions', arXiv preprint arXiv:1903.05054.

Pocuca, N., Jevtic, P., McNicholas, P.D. and Miljkovic, T. (2018), 'Modeling frequency and severity of claims with the zero-inflated generalized cluster-weighted models', arXiv preprint arXiv:1812.11829.

Bierling, V.S.E. and McNicholas, P.D. (2018), 'Latent Gaussian mixture model for clustering longitudinal data', arXiv preprint arXiv:1804.05133.

**Articles In Press**Murray, P.M., Browne, R.P. and McNicholas, P.D., 'Mixtures of hidden truncation hyperbolic factor analyzers',

Wei , Y., Tang, Y. and McNicholas, P.D., 'Flexible high-dimensional unsupervised learning with missing data'. *IEEE Transactions on Pattern Analysis and Machine Intelligence*. To appear. [doi] [preprint]

Gallaugher, M.P.B. and McNicholas, P.D., 'On fractionally-supervised classification: Weight selection and extension to the multivariate t-distribution', *Journal of Classification*. To appear. [doi] [preprint]

**2020**

Punzo, A., Blostein, M. and McNicholas, P.D. (2020), 'High-dimensional unsupervised classification via parsimonious contaminated mixtures', *Pattern Recognition* **98**:107031. [doi] [preprint]

**2019**

Turco, C.V., Pesevski, A., McNicholas, P.D., Beaulieu, L.-D. and Nelson, A.J. (2019), 'Reliability of transcranial magnetic stimulation measures of afferent inhibition', *Brain Research* **1723**:146394. [doi]

Silva, A., Rothstein, S.J., McNicholas, P.D. and Subedi, S. (2019), 'A multivariate Poisson-log normal mixture model for clustering transcriptome sequencing data', *BMC Bioinformatics* **20**:394. [doi] (open access)

Tortora, C., Franczak, B.C., Browne, R.P. and McNicholas, P.D. (2019), 'A mixture of coalesced generalized hyperbolic distributions', *Journal of Classification* **36**(1), 26-57. [doi] [preprint]

McNicholas, P.D. (2019), 'Data science', *FACETS* **4**(1), 131-135. [doi] (open access)

Murray, P.M., Browne, R.P. and McNicholas, P.D. (2019), Note of Clarification on 'Hidden truncation hyperbolic distributions, finite mixtures thereof, and their application for clustering, by Murray, Browne, and McNicholas, J. Multivariate Analysis 161 (2017) 141-156.', *Journal of Multivariate Analysis* **171**, 475-476 [doi]

Morris, K., Punzo, A., McNicholas, P.D. and Browne, R.P. (2019), 'Asymmetric clusters and outliers: Mixtures of multivariate contaminated shifted asymmetric Laplace distributions', *Computational Statistics and Data Analysis* **132**, 145-166. [doi] [preprint]

Mayhew, A.J., Amog, K., Phillips, S., Parise, G., McNicholas, P.D., de Souza, R.J., Thabane, L. and Raina P. (2019), 'The prevalence of sarcopenia in community dwelling older adults, an exploration of differences between studies and within definitions: A systematic review and meta-analyses', *Age and Aging*. **48**(1), 48-56. [doi]

Gallaugher, M.P.B. and McNicholas, P.D. (2019), 'Three skewed matrix variate distributions', *Statistics and Probability Letters* **145**, 103-109. [doi] [preprint]

Wei, Y., Tang, Y. and McNicholas, P.D. (2019), 'Mixtures of generalized hyperbolic distributions and mixtures of skew-t distributions for model-based clustering with incomplete data', *Computational Statistics and Data Analysis* **130**, 18-41. [doi] [preprint]

**2018**

Shaikh, M.R., McNicholas, P.D., Antonie, L.M. and Murphy, T.B. (2018), 'Standardizing interestingness measures for association rules', *Statistical Analysis and Data Mining* **11**(6), 282-295. [doi]

Jones, A., Costa, A.P., Pesevski, A. and McNicholas, P.D. (2018), 'Predicting hospital and·emergency department utilization among community-dwelling older adults: statistical and machine learning approaches', *PLOS ONE* **13**(11): e0206662. [doi] (open access)

Morton, R.W., Sato, K., Gallaugher, M.P.B., Oikawa, S.Y., McNicholas, P.D., Fujita, S. and Phillips, S.M. (2018), 'Muscle androgen receptor content but not systemic hormones is associated with resistance training-induced skeletal muscle hypertrophy in healthy, young men', *Frontiers in Physiology* **9**, 1373. [doi]

Pesevski, A., Franczak, B.C. and McNicholas, P.D. (2018), 'Subspace clustering with the multivariate-t distribution', *Pattern Recognition Letters* **112**(1), 297-302. [doi] [preprint]

Punzo, A. Mazza, A. and McNicholas, P.D. (2018), 'ContaminatedMixt: An R package for fitting parsimonious mixtures of multivariate contaminated normal distributions', *Journal of Statistical Software* **85**:10. [doi] (open access)

McNicholas, P.D., McNicholas, S.M. and Tait, P.A. (2018), Discussion of 'Statistical challenges of administrative and transaction data’ by Hand, *Journal of the Royal Statistical Society: Series A ***181**(3), 594-595. [doi]

Gallaugher, M.P.B. and McNicholas, P.D. (2018), 'Finite mixtures of skewed matrix variate distributions', *Pattern Recognition* **80**, 83-93. [doi] [preprint]

Tang, Y, Browne, R.P. and McNicholas, P.D. (2018), 'Flexible clustering of high-dimensional data via mixtures of joint generalized hyperbolic distributions', *Stat* **7**(1), e177. [doi] [preprint]

Andrews, J.L, Wickins, J.R., Boers, N.M. and McNicholas, P.D. (2018), 'teigen: An R package for model-based clustering and classification via the multivariate t distribution', *Journal of Statistical Software* **83**:7. [doi] (open access)

**2017**

Skinnider, M.A., Dejong, C.A., Franczak, B.C., McNicholas, P.D. and Magarvey, N.A. (2017), 'Comparative analysis of chemical similarity methods for modular natural products with a hypothetical structure enumeration algorithm', *Journal of Cheminformatics* **9**:46. [doi] (open access)

Murray, P.M., Browne, R.P. and McNicholas, P.D. (2017), 'Hidden truncation hyperbolic distributions, finite mixtures thereof, and their application for clustering', *Journal of Multivariate Analysis* **161**, 141-156. [doi] (open access)

Punzo, A. and McNicholas, P.D. (2017), 'Robust clustering in regression analysis via the contaminated Gaussian cluster-weighted model', *Journal of Classification* **34**(2), 249-293. [doi]

Murray, P.M., Browne, R.P. and McNicholas, P.D. (2017), 'A mixture of SDB skew-t factor analyzers', *Econometrics and Statistics* **3**, 160-168. [doi] [preprint]

Gallaugher, M.P.B. and McNicholas, P.D. (2017), Discussion of 'Random-projection ensemble classification’ by Cannings and Samworth, *Journal of the Royal Statistical Society: Series B* **79**(4), 1011-1012. [doi]

Gallaugher, M.P.B. and McNicholas, P.D. (2017), 'A matrix variate skew-t distribution', *Stat* **6**(1), 160-170. [doi] [preprint]

Dang, U.J., Punzo, A., McNicholas, P.D., Ingrassia, S. and Browne, R.P. (2017), 'Multivariate response and parsimony for Gaussian cluster-weighted models', *Journal of Classification* **34**(1), 4-34. [doi]

Cheam, A.S.M., Marbac, M., and McNicholas, P.D. (2017), 'Model-based clustering for spatio-temporal data on air quality monitoring', *Environmetrics* **28**(3), e2437. [doi]

McNicholas, S.M., McNicholas, P.D., and Browne, R.P. (2017), A mixture of variance-gamma factor analyzers. In Ahmed, S.E., editor, *Big and Complex Data Analysis.* Cham: Springer International Publishing, pp. 369-385. [doi]

Wong, M.H.T., Mutch, D.M., and McNicholas, P.D. (2017), 'Two-way learning with one-way supervision for gene expression data', *BMC Bioinformatics ***18**:150. [doi]

**2016**

Franczak, B.C., Castura, J.C., Browne, R.P., Findlay, C.J. and McNicholas, P.D. (2016) 'Handling missing data in consumer hedonic tests arising from direct scaling: Imputation techniques for consumer hedonic tests', *Journal of Sensory Studies* **31**(6), 514-523. [doi]

Marbac, M. and McNicholas, P.D. (2016), 'Dimension reduction in clustering', *Wiley StatsRef: Statistics Reference Online*. [doi].

Tortora, C., McNicholas, P.D. and Browne, R.P. (2016), 'A mixture of generalized hyperbolic factor analyzers', *Advances in Data Analysis and Classification* **10**(4), 423-440. [doi]

McNicholas, P.D. (2016), 'Model-based clustering', *Journal of Classification* **33**(3), 331-373. [doi] (open access)

Punzo, A. and McNicholas, P.D. (2016), 'Parsimonious mixtures of multivariate contaminated normal distributions', *Biometrical Journal* **58**(6), 1506-1537. [doi]

Punzo, A., Browne, R.P. and McNicholas, P.D. (2016), 'Hypothesis testing for mixture model selection', *Journal of Statistical Computation and Simulation* **86**(14), 2797-2818. [doi]

Azzalini, A., Browne, R.P., Genton, M.G. and McNicholas, P.D. (2016), 'On nomenclature for, and the relative merits of, two formulations of skew distributions', *Statistics and Probability Letters* **110**, 201-206 [doi]

McNicholas, P.D. and Subedi, S. (2016), Discussion of 'Perils and potentials of self-selected entry to epidemiological studies and surveys' by Keiding ad Louis, *Journal of the Royal Statistical Society: Series A* **179**(2), 362-363. [doi]

Morris, K. and McNicholas, P.D. (2016), 'Clustering, classification, discriminant analysis, and dimension reduction via generalized hyperbolic mixtures', *Computational Statistics and Data Analysis* **97** 133-150. [doi]

Cheam, A.S.M. and McNicholas, P.D. (2016), 'Modelling receiver operating characteristic curves using Gaussian mixtures', *Computational Statistics and Data Analysis* **93**, 192-206. [doi]

O’Hagan, A., Murphy, T.B., Gormley, I.C., McNicholas, P.D. and Karlis, D. (2016), ‘Clustering with the multivariate normal inverse Gaussian distribution’, *Computational Statistics and Data Analysis* **93**, 18-30. [doi]

**2015**

Dang, U.J., Browne, R.P. and McNicholas, P.D. (2015), 'Mixtures of multivariate power exponential distributions', *Biometrics* **71**(4), 1081-1089. [doi]

Vrbik, I. and McNicholas, P.D. (2015), 'Fractionally-supervised classification', *Journal of Classification* **32**(3), 359-381. [doi]

Subedi, S., Punzo, A., Ingrassia, S. and McNicholas, P.D. (2015), 'Cluster-weighted t-factor analyzers for robust model-based clustering and dimension reduction', *Statistical Methods and Applications* **24**(4), 623-649. [doi]

Browne, R.P. and McNicholas, P.D. (2015), 'Multivariate sharp quadratic bounds via Σ-strong convexity and the Fenchel connection', *Electronic Journal of Statistics* **9**(2), 1913-1938. [doi]

Wei, Y. and McNicholas, P.D. (2015), 'Mixture model averaging for clustering', *Advances in Data Analysis and Classification* **9**(2), 197-217. [doi]

Browne, R.P. and McNicholas, P.D. (2015), 'A mixture of generalized hyperbolic distributions', *Canadian Journal of Statistics ***43**(2), 176-198. [doi]

Franczak, B.C., Browne, R.P., McNicholas, P.D. and Findlay, C.J. (2015),** **'Product Selection for Liking Studies: The Sensory Informed Design', *Food Quality and Preference* **44**, 36-43. [doi]

Franczak, B.C., Tortora, C., Browne, R.P. and McNicholas, P.D. (2015), 'Unsupervised learning via mixtures of skewed distributions with hypercube contours', *Pattern Recognition Letters* **58**(1), 69-76. [doi]

Dang, U.J and McNicholas, P.D. (2015), Families of parsimonious finite mixtures of regression models. In: Morlini, I., Minerva T. and Vichi, M., editors, *Advances in Statistical Models for Data Analysis*, Studies in Classification, Data Analysis, and Knowledge Organization. Cham: Springer International Publishing, pp. 73-84. [eBook]

Tang, Y., Browne, R.P. and McNicholas, P.D. (2015), ‘Model-based clustering of high-dimensional binary data’, *Computational Statistics and Data Analysis* **87**, 84-101. [doi]

Subedi, S. and McNicholas, P.D. (2015), Discussion of 'Analysis of forensic DNA mixtures with artefacts' by Cowell et al., *Journal of the Royal Statistical Society: Series C* **64**(1), 43-44. [doi]

**2014**

Coneva, V., Simopoulos, C., Casaretto, J.A., El-kereamy, A., Guevara, D.R., Cohn, J., Zhu, T., Guo, L., Alexander, D.C., Bi, Y.-M., McNicholas, P.D. and Rothstein, S.J. (2014), ‘Metabolic and co-expression network-based analyses associated with nitrate response in rice’, *BMC Genomics* **15**:1056. [doi]

Ralston, J., Badoud, F., Cattrysse, B., McNicholas, P.D. and Mutch, D.M. (2014), 'Inhibition of stearoyl-CoA desaturase-1 in differentiating 3T3-L1 pre-adipocytes up-regulates Elongase 6 and down-regulates genes affecting triacylglycerol synthesis', *International Journal of Obesity*. **38**, 1449-1456. [doi]

Misyura, M., Guevara, D., Subedi, S., Hudson, D., McNicholas, P.D., Colasanti, J. and Rothstein, S.J. (2014), 'Nitrogen limitation and high density responses in rice suggest a role for ethylene in intraspecific competition', *BMC Genomics* **15**:681. [doi]

Andrews, J.L. and McNicholas, P.D. (2014), 'Variable selection for clustering and classification', *Journal of Classification* **31**(2), 136-153. [doi]

Franczak, B.C., Browne, R.P. and McNicholas, P.D. (2014), 'Mixtures of shifted asymmetric Laplace distributions', *IEEE Transactions on Pattern Analysis and Machine Intelligence* **36**(6), 1149-1157. [doi]

Browne, R.P. and McNicholas, P.D. (2014), 'Estimating common principal components in high dimensions', *Advances in Data Analysis and Classification* **8**(2), 217-226. [doi]

Subedi, S. and McNicholas, P.D. (2014), 'Variational Bayes approximations for clustering via mixtures of normal inverse Gaussian distributions', *Advances in Data Analysis and Classification* **8**(2), 167-193. [doi]

Murray, P.M., Browne, R.P. and McNicholas, P.D. (2014), 'Mixtures of skew-t factor analyzers', *Computational Statistics and Data Analysis ***77**, 326-335 (open access). [doi]

Murray, P.M., McNicholas, P.D. and Browne, R.P. (2014), 'A mixture of common skew-t factor analyzers', *Stat* **3**(1), 68-82. [doi]

Bhattacharya, S. and McNicholas, P.D. (2014), 'A LASSO-penalized BIC for mixture model selection', *Advances in Data Analysis and Classification ***8**(1), 45-61. [doi]

Lin, T.-I., McNicholas, P.D. and Hsiu, J.H. (2014), 'Capturing patterns via parsimonious t mixture models', *Statistics and Probability Letters* **88**, 80-87. [doi]

Browne, R.P. and McNicholas, P.D. (2014), 'Orthogonal Stiefel manifold optimization for eigen-decomposed covariance parameter estimation in mixture models', *Statistics and Computing* **24**(2), 203-210. [doi].

Xia, Y. and McNicholas, P.D. (2014), 'A gradient method for the monotone fused least absolute shrinkage and selection operator', *Optimization Methods and Software* **29**(3), 463-483. [doi]

Vrbik, I. and McNicholas, P.D. (2014), 'Parsimonious skew mixture models for model-based clustering and classification', *Computational Statistics and Data Analysis* **71**, 196-210. [doi]

**2013**McNicholas, P.D., Browne, R.P. and Murray, P.M. (2013), Discussion of 'Model-based clustering and classification with non-normal mixture distributions' by Lee and McLachlan,

Browne, R.P. and McNicholas, P. D. (2013), Mixture and latent class models in longitudinal and other settings. In Scott, M.A., Simonoff, J.S., and Marx, B.D., editors, *The SAGE Handbook of Multilevel Modelling*. SAGE Publications Ltd., pp. 357-370.

McNicholas, P. D. (2013). On clustering and classification via mixtures of multivariate t-distributions. In Guidici, P., Ingrassia, S. and Vichi, M., editors, *Statistical Models for Data Analysis*, Studies in Classification, Data Analysis, and Knowledge Organization. Cham: Springer International Publishing, pp. 233-240.

Morris, K., McNicholas, P.D. and Scrucca, L. (2013), 'Dimension reduction for model-based clustering via mixtures of multivariate t-distributions', *Advances in Data Analysis and Classification* **7**(3), 321-338. [doi]

Morris, K. and McNicholas, P.D. (2013), 'Dimension reduction for model-based clustering via mixtures of shifted asymmetric Laplace distributions', *Statistics and Probability Letters* **83**(9), 2088-2093. [doi] [erratum]

McNicholas, P.D. and Browne, R.P. (2013), Discussion of 'How to find an appropriate clustering for mixed-type variables with application to socio-economic stratification' by Hennig and Liao, *Journal of the Royal Statistical Society: Series C* **62**(3), 352-353. [doi]

Liseron-Monfils, C., Lewis, T., Ashlock, D., McNicholas, P.D., Fauteux, F., Stromvik, M. and Raizada, M.N. (2013), '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', *BMC Plant Biology* **13**:42. [doi]

Andrews, J.L. and McNicholas, P.D. (2013), 'Using evolutionary algorithms for model-based clustering ', *Pattern Recognition Letters* **34**(9), 987-992. [doi]

Humbert, S., Subedi, S., Cohn, J., Zeng, B., Bi, Y.-M., Chen, X., Zhu, T., McNicholas, P.D., and Rothstein, S.J. (2013), 'Genome-wide expression profiling of maize in response to individual and combined water and nitrogen stresses', *BMC Genomics* **14**(3) [doi].

Subedi, S., Punzo, A., Ingrassia, S. and McNicholas, P.D. (2013), 'Clustering and classification via cluster-weighted factor analyzers', *Advances in Data Analysis and Classification* **7**(1), 5-40. [doi]

**2012**Wong, M.H.T., Holst, C., Astrup, A., Handjieva-Darlenska, T., Jebb, S.A., Kafatos, A., Kunesova, M., Larsen, T.M., Martinez, D.M., Pfeiffer, A.F.H., van Baak, M.A., Saris, W.H.M., McNicholas, P.D. and Mutch, D.M. (2012), 'Caloric restriction induces changes in insulin and body weight measurements that are inversely associated with subsequent weight regain',

Zulyniak, M.A., Ralston, J.C., Tucker, A.J., MacKay, K.A., Hillyer, L.M., McNicholas, P.D., Graham, T.E., Robinson, L.E., Duncan, A.M., Ma, D.W.L. and Mutch, D.M. (2012), 'Vaccenic acid in serum triglycerides is associated with markers of insulin resistance in men', *Applied Physiology, Nutrition, and Metabolism* **37**(5), 1003-1007. [doi]

Browne, R.P. and McNicholas, P.D. (2012), 'Model-based clustering and classification of data with mixed type', *Journal of Statistical Planning and Inference* **142**(11), 2976-2984. [doi]

Vrbik, I. and McNicholas, P.D. (2012), 'Analytic calculations for the EM algorithm for multivariate skew-t mixture models', *Statistics and Probability Letters* **82**(6), 1169-1174. [doi]

McNicholas, P.D. and Subedi, S. (2012), 'Clustering gene expression time course data using mixtures of multivariate t-distributions', *Journal of Statistical Planning and Inference* **142**(5), 1114-1127. [doi]

Feng, Z.Z., Yang, X., Subedi, S. and McNicholas, P.D. (2012), 'The LASSO and sparse least squares regression methods for SNP selection in predicting quantitative traits', *IEEE Transactions on Computational Biology and Bioinformatics* **9**(2), 629-636. [doi]

Browne, R.P., McNicholas, P.D. and Sparling, M.D. (2012), 'Model-based learning using a mixture of mixtures of Gaussian and uniform distributions', *IEEE Transactions on Pattern Analysis and Machine Intelligence* **34**(4), 814-817. [doi]

Andrews, J.L. and McNicholas, P.D. (2012), 'Model-based clustering, classification, and discriminant analysis via mixtures of multivariate t-distributions', *Statistics and Computing* **22**(5), 1021-1029. [doi]

Steane, M.A., McNicholas, P.D. and Yada, R. (2012), 'Model-based classification via mixtures of multivariate t-factor analyzers'. *Communications in Statistics -- Simulation and Computation* **41**(4), 510-523. [doi]

**2011**McNicholas, P.D. (2011), 'On model-based clustering, classication, and discriminant analysis',

McNicholas, P.D., Murphy, T.B., Jampani, K.R., McDaid, A.F., and Banks, L. (2011), 'pgmm Version 1.0 for R: Model-based clustering and classification via latent Gaussian mixture models'. Technical Report 2011-320, Department of Mathematics and Statistics, University of Guelph. December 2011. [pdf]

Xu, R., McNicholas, P.D., Desmond, A.F. and Darlington, G.A. (2011), 'A first passage time model for long term survivors with competing risks', *The International Journal of Biostatistics* **7**(1), Article 26. [doi]

Andrews, J.L. and McNicholas, P.D. (2011), 'Mixtures of modified t-factor analyzers for model-based clustering, classification, and discriminant analysis', *Journal of Statistical Planning and Inference* **141**(4), 1479-1486. [doi]

Ashlock, D., Schonfeld, J. and McNicholas, P.D. (2011), 'Translation tables: A genetic code in a evolutionary algorithm'. In IEEE Congress on Evolutionary Computation (CEC), New Orleans, pp. 2685-2692. [doi]

Andrews, J.L. and McNicholas, P.D. (2011), 'Extending mixtures of multivariate t-factor analyzers', *Statistics and Computing* **21**(3), 361-373. [doi]

Andrews, J.L., McNicholas, P.D. and Subedi, S. (2011), 'Model-based classification via mixtures of multivariate t-distributions', *Computational Statistics and Data Analysis* **55**(1), 520-529. [doi]

Balka, J., Desmond, A.F. and McNicholas, P.D. (2011), 'Bayesian and likelihood inference for cure rates based on defective inverse Gaussian regression models', *Journal of Applied Statistics* **38**(1), 127-144. [doi]

**2010**McNicholas, P.D. and Murphy, T.B. (2010), 'Model-based clustering of microarray expression data via latent Gaussian mixture models',

Shaikh, M., McNicholas, P.D. and Desmond, A.F. (2010), 'A pseudo-EM algorithm for clustering incomplete longitudinal data', *The International Journal of Biostatistics* **6**(1), Article 8. [doi]

McNicholas, P.D. and Murphy, T.B. (2010), 'Model-based clustering of longitudinal data', *The Canadian Journal of Statistics* **38**(1), 153-168. [doi]

McNicholas, P.D. (2010), 'Model-based classification using latent Gaussian mixture models', *Journal of Statistical Planning and Inference* **140**(5), 1175-1181. [doi]

McNicholas, P.D., Murphy, T.B., McDaid, A.F. and Frost, D. (2010), 'Serial and parallel implementations of model-based clustering via parsimonious Gaussian mixture models', *Computational Statistics and Data Analysis* **54**(3), 711-723. [doi]

**2007-2009**Balka, J., Desmond, A.F. and McNicholas, P.D. (2009), 'Review and implementation of cure models based on first hitting times for Wiener processes',

McNicholas, P.D. and Zhao, Y.C. (2009), Association rules: An overview, in Y. Zhao, C. Zhang & L. Cao, eds, 'Post-Mining of Association Rules: Techniques for Effective Knowledge Extraction', IGI Global, pp. 1-10.

Fu, Y., Kim, L.-T. and McNicholas, P.D. (2009), 'Changes on enological parameters of white wine packaged in bag-in-box during secondary shelf life'. *Journal of Food Science* **74**(8), C608-C618. [doi]

McNicholas, P.D. and Murphy, T.B. (2008), 'Parsimonious Gaussian mixture models', *Statistics and Computing* **18**(3), 285-296. [doi]

McNicholas, P.D., Murphy, T.B. and O'Regan, M. (2008), 'Standardising the lift of an association rule', *Computational Statistics and Data Analysis* **52**(10), 4712-4721. [doi]

McNicholas, P.D. (2007), 'Association rule analysis of CAO data (with discussion)', *Journal of the Statistical and Social Inquiry Society of Ireland* **36**, 44-83.

Ahmad, K., Rogers, S., McNicholas, P.D. and Collins P. (2007), 'Narrowband UVB and PUVA in the treatment of mycosis fungoides: A retrospective study', *Acta Dermato-Venereologica* **87**(5), 413-417. [doi]

**Opinion**McNicholas, P.D. (2016), 'Turning the spit: A perspective on the NSERC Discovery Grant review process',

**Special Issue Editorials**Einbeck, J., Hinde, J., Ingrassia, S., Lin, T.-I. and McNicholas, P.D. (2019), 'Editorial for the 4th Special Issue on advances in mixture models',

Kestler, H.A., McNicholas, P.D. and Wilhelm, A.F.X. (2018), 'Special issue on "Science of big data: theory, methods and applications"', *Advances in Data Analysis and Classification* **12**(4), :823-825. [doi]

Hinde, J., Ingrassia, S., Lin, T.-I. and McNicholas, P. (2016), 'The third special issue on advances in mixture models' *Computational Statistics and Data Analysis* **93**, 2-4. [doi]

Böhning, D., Hennig, C., McLachlan, G.J., McNicholas, P.D. (2014), 'The 2nd special issue on advances in mixture models', *Computational Statistics and Data Analysis* **71**, 1-2. [doi]

**Book Review**McNicholas, P.D. (2009), 'Multivariate and Probabilistic Analyses of Sensory Science Problems',

Paul McNicholas