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2015 Book Chapter Computation: Expectation-Maximization AlgorithmMcLachlan, Geoffrey J. (2015). Computation: Expectation-Maximization Algorithm. International Encyclopedia of the Social & Behavioral Sciences: Second Edition. (pp. 469-474) Amsterdam, Netherlands: Elsevier . doi: 10.1016/B978-0-08-097086-8.42007-6 |
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2015 Book Chapter Multivariate Analysis: Classification and DiscriminationMcLachlan, Geoffrey (2015). Multivariate Analysis: Classification and Discrimination. International Encyclopedia of the Social & Behavioral Sciences: Second Edition. (pp. 116-120) Amsterdam, Netherlands: Elsevier . doi: 10.1016/B978-0-08-097086-8.42150-1 |
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2015 Journal Article Nature and man: the goal of bio-security in the course of rapid and inevitable human developmentPyne, Saumyadipta, Lee, Sharon X. and McLachlan, Geoffrey J. (2015). Nature and man: the goal of bio-security in the course of rapid and inevitable human development. Journal of the Indian Society of Agricultural Statistics, 69 (2), 117-125. |
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2015 Journal Article Inference on differences between classes using cluster-specific contrasts of mixed effectsNg, Shu Kay, McLachlan, Geoffrey J., Wang, Kui, Nagymanyoki, Zoltan, Liu, Shubai and Ng, Shu-Wing (2015). Inference on differences between classes using cluster-specific contrasts of mixed effects. Biostatistics, 16 (1), 98-112. doi: 10.1093/biostatistics/kxu028 |
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2015 Book Chapter Mixture Models in StatisticsMcLachlan, Geoffrey J. (2015). Mixture Models in Statistics. International Encyclopedia of the Social & Behavioral Sciences: Second Edition. (pp. 624-628) Amsterdam, Netherlands: Elsevier . doi: 10.1016/B978-0-08-097086-8.42055-6 |
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2014 Journal Article A robust factor analysis model using the restricted skew-t distributionLin, Tsung-I, Wu, Pal H., McLachlan, Geoffrey J. and Lee, Sharon X. (2014). A robust factor analysis model using the restricted skew-t distribution. Test, 24 (3), 510-531. doi: 10.1007/s11749-014-0422-2 |
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2014 Journal Article On the number of components in a Gaussian mixture modelMcLachlan, Geoffrey J. and Rathnayake, Suren (2014). On the number of components in a Gaussian mixture model. Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery, 4 (5), 341-355. doi: 10.1002/widm.1135 |
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2014 Journal Article Joint modeling and registration of cell populations in cohorts of high-dimensional flow cytometric dataPyne, Saumyadipta, Lee, Sharon X., Wang, Kui, Irish, Jonathan, Tamayo, Pablo, Nazaire, Marc-Danie, Duong, Tarn, Ng, Shu-Kay, Hafler, David, Levy, Ronald, Nolan, Garry P., Mesirov, Jill and McLachlan, Geoffrey J. (2014). Joint modeling and registration of cell populations in cohorts of high-dimensional flow cytometric data. PLoS One, 9 (7) e100334, e100334.1-e100334.11. doi: 10.1371/journal.pone.0100334 |
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2014 Journal Article False discovery rate control in magnetic resonance imaging studies via Markov random fieldsNguyen, Hien D., McLachlan, Geoffrey J., Cherbuin, Nicolas and Janke, Andrew L. (2014). False discovery rate control in magnetic resonance imaging studies via Markov random fields. IEEE Transactions on Medical Imaging, 33 (8) 6811158, 1735-1748. doi: 10.1109/TMI.2014.2322369 |
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2014 Journal Article Mixture models for clustering multilevel growth trajectoriesNg S.K. and McLachlan G.J. (2014). Mixture models for clustering multilevel growth trajectories. Computational Statistics and Data Analysis, 71, 43-51. doi: 10.1016/j.csda.2012.12.007 |
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2014 Journal Article Finite mixtures of multivariate skew t-distributions: Some recent and new resultsLee, Sharon and McLachlan, Geoffrey J. (2014). Finite mixtures of multivariate skew t-distributions: Some recent and new results. Statistics and Computing, 24 (2), 181-202. doi: 10.1007/s11222-012-9362-4 |
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2014 Journal Article The 2nd special issue on advances in mixture modelsBoehning, Dankmar, Hennig, Christian, McLachlan, Geoffrey J. and McNicholas, Paul D. (2014). The 2nd special issue on advances in mixture models. Computational Statistics and Data Analysis, 71, 1-2. doi: 10.1016/j.csda.2013.10.010 |
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2014 Conference Publication Asymptotic inference for hidden process regression modelsNguyen, Hien D. and McLachlan, Geoffrey J. (2014). Asymptotic inference for hidden process regression models. 2014 IEEE Workshop on Statistical Signal Processing (SSP 2014), Gold Coast, Australia, 29 June - 2 July 2014. Piscataway, NJ, United States: IEEE. doi: 10.1109/SSP.2014.6884624 |
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2014 Conference Publication Mixture of regression models with latent variables and sparse coefficient parametersNg, Shu-Kay and McLachlan, Geoffrey J. (2014). Mixture of regression models with latent variables and sparse coefficient parameters. COMPSTAT 2014, Geneva Switzerland, 19- 22 August 2014. Hague, Netherlands: The International Statistical Institute/International Association for Statistical Computing. |
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2014 Conference Publication Making sense of a random world through statisticsMcLachlan, Geoff (2014). Making sense of a random world through statistics. AusDM 2014, Brisbane, QLD, Australia, 27-28 November 2014. Darlinghurst, NSW, Australia: Australian Computer Society. |
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2014 Conference Publication Application of multiple imputation to incomplete three-way three-mode multi-environment trial dataTian, T., McLachlan, G., Dieters, M. and Basford, K. (2014). Application of multiple imputation to incomplete three-way three-mode multi-environment trial data. International Biometric Conference, Florence (Italy), 6-11 July 2014. Florence, Italy: International Biometric Society. |
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2013 Journal Article EMMIXuskew: An R package for Fitting Mixtures of Multivariate Skew t distributions via the EM algorithmLee S.X. and McLachlan G.J. (2013). EMMIXuskew: An R package for Fitting Mixtures of Multivariate Skew t distributions via the EM algorithm. Journal of Statistical Software, 55 (12), 1-22. doi: 10.18637/jss.v055.i12 |
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2013 Journal Article Rejoinder to the discussion of "Model-based clustering and classification with non-normal mixture distributions"Lee, Sharon X. and McLachlan, Geoffrey J. (2013). Rejoinder to the discussion of "Model-based clustering and classification with non-normal mixture distributions". Statistical Methods and Applications, 22 (4), 473-479. doi: 10.1007/s10260-013-0249-0 |
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2013 Journal Article Model-based clustering and classification with non-normal mixture distributionsLee, Sharon X. and McLachlan, Geoffrey J. (2013). Model-based clustering and classification with non-normal mixture distributions. Statistical Methods and Applications, 22 (4), 427-454. doi: 10.1007/s10260-013-0237-4 |
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2013 Journal Article On mixtures of skew normal and skew t-distributionsLee, Sharon X. and McLachlan, Geoffrey J. (2013). On mixtures of skew normal and skew t-distributions. Advances in Data Analysis and Classification, 7 (3), 241-266. doi: 10.1007/s11634-013-0132-8 |
