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2016 Journal Article Progress on a conjecture regarding the triangular distributionNguyen, Hien D. and McLachlan, Geoffrey J. (2016). Progress on a conjecture regarding the triangular distribution. Communications in Statistics: Theory and Methods, 46 (22), 11261-11271. doi: 10.1080/03610926.2016.1263742 |
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2016 Journal Article Linear mixed models with marginally symmetric nonparametric random effectsNguyen, Hien D. and McLachlan, Geoffrey J. (2016). Linear mixed models with marginally symmetric nonparametric random effects. Computational Statistics and Data Analysis, 103, 151-169. doi: 10.1016/j.csda.2016.05.005 |
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2016 Journal Article Spatial clustering of time series via mixture of autoregressions models and Markov random fieldsNguyen, Hien D., McLachlan, Geoffrey J., Ullmann, Jeremy F. P. and Janke, Andrew L. (2016). Spatial clustering of time series via mixture of autoregressions models and Markov random fields. Statistica Neerlandica, 70 (4), 414-439. doi: 10.1111/stan.12093 |
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2016 Journal Article Maximum likelihood estimation of triangular and polygonal distributionsNguyen, Hien D. and McLachlan, Geoffrey J. (2016). Maximum likelihood estimation of triangular and polygonal distributions. Computational Statistics and Data Analysis, 102, 23-36. doi: 10.1016/j.csda.2016.04.003 |
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2016 Journal Article Comment on "On nomenclature for, and the relative merits of, two formulations of skew distributions," by A. Azzalini, R. Browne, M. Genton, and P. McNicholasMcLachlan, Geoffrey J. and Lee, Sharon X. (2016). Comment on "On nomenclature for, and the relative merits of, two formulations of skew distributions," by A. Azzalini, R. Browne, M. Genton, and P. McNicholas. Statistics & Probability Letters, 116, 1-5. doi: 10.1016/j.spl.2016.04.004 |
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2016 Journal Article A block minorization-maximization algorithm for heteroscedastic regressionNguyen, Hien D., Lloyd-Jones, Luke R. and McLachlan, Geoffrey J. (2016). A block minorization-maximization algorithm for heteroscedastic regression. IEEE Signal Processing Letters, 23 (8) 7501879, 1131-1135. doi: 10.1109/LSP.2016.2586180 |
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2016 Journal Article Finite mixtures of canonical fundamental skew t-distributions: The unification of the restricted and unrestricted skew t-mixture modelsLee, Sharon X and McLachlan, Geoffrey J (2016). Finite mixtures of canonical fundamental skew t-distributions: The unification of the restricted and unrestricted skew t-mixture models. Statistics and Computing, 26 (3), 573-589. doi: 10.1007/s11222-015-9545-x |
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2016 Journal Article Laplace mixture autoregressive modelsNguyen, Hien D., McLachlan, Geoffrey J., Ullmann, Jeremy F. P. and Janke, Andrew L. (2016). Laplace mixture autoregressive models. Statistics and Probability Letters, 110, 18-24. doi: 10.1016/j.spl.2015.11.006 |
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2016 Conference Publication On mixture modelling with multivariate skew distributionsLee, Sharon X. and McLachlan, Geoffrey J. (2016). On mixture modelling with multivariate skew distributions. COMPSTAT: International Conference on Computational Statistics, Oviedo, Spain, 23-26 August 2016. The Hague, The Netherlands: The International Statistical Institute/International Association for Statistical Computing. |
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2016 Conference Publication A simple parallel EM algorithm for statistical learning via mixture modelsLee, Sharon X., Leemaqz, Kaleb L. and McLachlan, Geoffrey J. (2016). A simple parallel EM algorithm for statistical learning via mixture models. International Conference on Digital Image Computing, Gold Coast, QLD, Australia, 30 November - 2 December,2016. Piscataway, NJ, United States: IEEE (Institute for Electrical and Electronic Engineers). doi: 10.1109/DICTA.2016.7796997 |
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2016 Book Chapter Application of mixture models to large datasetsLee, Sharon X., McLachlan, Geoffrey J. and Pyne, Saumyadipta (2016). Application of mixture models to large datasets. Big data analytics: methods and applications. (pp. 57-74) edited by Saumyadipta Pyne, B. L. S. Prakasa Rao and S. B. Rao. New Delhi, India: Springer India. doi: 10.1007/978-81-322-3628-3_4 |
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2016 Journal Article Laplace mixture of linear expertsNguyen, Hien D. and McLachlan, Geoffrey J. (2016). Laplace mixture of linear experts. Computational Statistics and Data Analysis, 93, 177-191. doi: 10.1016/j.csda.2014.10.016 |
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2016 Journal Article Modeling of inter-sample variation in flow cytometric data with the joint clustering and matching procedureLee, Sharon X., McLachlan, Geoffrey J. and Pyne, Saumyadipta (2016). Modeling of inter-sample variation in flow cytometric data with the joint clustering and matching procedure. Cytometry Part A, 89 (1), 30-43. doi: 10.1002/cyto.a.22789 |
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2016 Book Chapter Mixture models for standard p-dimensional Euclidean dataMcLachlan, Geoffrey J. and Rathnayake, Suren I. (2016). Mixture models for standard p-dimensional Euclidean data. Handbook of cluster analysis. (pp. 145-171) edited by Christian Hennig, Marina Meila, Fionn Murtagh and Roberto Rocci. Boca Raton, FL, United States: CRC Press. doi: 10.1201/b19706-14 |
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2016 Conference Publication Robust estimation of mixtures of skew-normal distributionsGarcía-Escudero, L. A., Greselin, F., Mayo-Iscar, A. and McLachlan, G. J. (2016). Robust estimation of mixtures of skew-normal distributions. Scientific Meeting of the Italian Statistical Society, Salerno, Italy, 8-10 November 2016. Fisciano, Italy: Dipartimento di Scienze Economiche e Statistiche, University of Salerno.. |
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2016 Conference Publication Finding group structures in "Big Data" in healthcare research using mixture modelsNg, Shu-Kay and McLachlan, Geoffrey J. (2016). Finding group structures in "Big Data" in healthcare research using mixture models. IEEE International Conference on Bioinformatics and Biomedicine, Shenzhen, China, 15-18 December 2016. Piscataway, NJ, United States: IEE Computer Society. doi: 10.1109/BIBM.2016.7822692 |
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2016 Book Chapter Mixture distributions - further developmentsMcLachlan, Geoffrey J. (2016). Mixture distributions - further developments. Wiley statsref: statistics reference online. (pp. 1-13) Chichester, United Kingdom: John Wiley & Sons. doi: 10.1002/9781118445112.stat00947.pub2 |
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2016 Journal Article A benchmark for evaluation of algorithms for identification of cellular correlates of clinical outcomesAghaeepour, Nima, Chattopadhyay, Pratip, Chikina, Maria, Dhaene, Tom, Van Gassen, Sofie, Kursa, Miron, Lambrecht, Bart N., Malek, Mehrnoush, McLachlan, G. J., Qian, Yu, Qiu, Peng, Saeys, Yvan, Stanton, Rick, Tong, Dong, Vens, Celine, Walkowiak, Slawomir, Wang, Kui, Finak, Greg, Gottardo, Raphael, Mosmann, Tim, Nolan, Garry P., Scheuermann, Richard H. and Brinkman, Ryan R. (2016). A benchmark for evaluation of algorithms for identification of cellular correlates of clinical outcomes. Cytometry Part A, 89 (1), 16-21. doi: 10.1002/cyto.a.22732 |
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2016 Journal Article Mixtures of spatial spline regressions for clustering and classificationNguyen, Hien D., McLachlan, Geoffrey J. and Wood, Ian A. (2016). Mixtures of spatial spline regressions for clustering and classification. Computational Statistics and Data Analysis, 93, 76-85. doi: 10.1016/j.csda.2014.01.011 |
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2016 Journal Article Extending mixtures of factor models using the restricted multivariate skew-normal distributionLin, Tsung-I, McLachlan, Geoffrey J. and Lee, Sharon X. (2016). Extending mixtures of factor models using the restricted multivariate skew-normal distribution. Journal of Multivariate Analysis, 143, 398-413. doi: 10.1016/j.jmva.2015.09.025 |
