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2016

Book Chapter

Application of mixture models to large datasets

Lee, 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

Application of mixture models to large datasets

2016

Journal Article

Laplace mixture of linear experts

Nguyen, 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

Laplace mixture of linear experts

2016

Journal Article

Modeling of inter-sample variation in flow cytometric data with the joint clustering and matching procedure

Lee, 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

Modeling of inter-sample variation in flow cytometric data with the joint clustering and matching procedure

2016

Book Chapter

Mixture models for standard p-dimensional Euclidean data

McLachlan, 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

Mixture models for standard p-dimensional Euclidean data

2016

Conference Publication

Robust estimation of mixtures of skew-normal distributions

García-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..

Robust estimation of mixtures of skew-normal distributions

2016

Book Chapter

Mixture distributions - further developments

McLachlan, 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

Mixture distributions - further developments

2016

Conference Publication

Finding group structures in "Big Data" in healthcare research using mixture models

Ng, 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

Finding group structures in "Big Data" in healthcare research using mixture models

2015

Journal Article

Application of multiple imputation for missing values in three-way three-mode multi-environment trial data

Tian, Ting, McLachlan, Geoffrey J., Dieter, Mark J. and Basford, Kaye E. (2015). Application of multiple imputation for missing values in three-way three-mode multi-environment trial data. PLoS One, 10 (12) e0144370, e0144370.1-e0144370.25. doi: 10.1371/journal.pone.0144370

Application of multiple imputation for missing values in three-way three-mode multi-environment trial data

2015

Journal Article

Special issue on "New trends on model-based clustering and classification"

Ingrassia, Salvatore, McLachlan, Geoffrey J. and Govaert, Gerard (2015). Special issue on "New trends on model-based clustering and classification". Advances in Data Analysis and Classification, 9 (4), 367-369. doi: 10.1007/s11634-015-0224-8

Special issue on "New trends on model-based clustering and classification"

2015

Edited Outputs

Advances in Data Analysis and Classification

Advances in Data Analysis and Classification. (2015). 9 (4)

Advances in Data Analysis and Classification

2015

Journal Article

Maximum likelihood estimation of Gaussian mixture models without matrix operations

Nguyen, Hien D. and McLachlan, Geoffrey J. (2015). Maximum likelihood estimation of Gaussian mixture models without matrix operations. Advances in Data Analysis and Classification, 9 (4), 371-394. doi: 10.1007/s11634-015-0209-7

Maximum likelihood estimation of Gaussian mixture models without matrix operations

2015

Journal Article

Inference on differences between classes using cluster-specific contrasts of mixed effects

Ng, 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

Inference on differences between classes using cluster-specific contrasts of mixed effects

2015

Book Chapter

Mixture Models in Statistics

McLachlan, 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

Mixture Models in Statistics

2015

Book Chapter

Computation: Expectation-Maximization Algorithm

McLachlan, 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

Computation: Expectation-Maximization Algorithm

2015

Book Chapter

Multivariate Analysis: Classification and Discrimination

McLachlan, 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

Multivariate Analysis: Classification and Discrimination

2015

Journal Article

Nature and man: the goal of bio-security in the course of rapid and inevitable human development

Pyne, 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.

Nature and man: the goal of bio-security in the course of rapid and inevitable human development

2014

Journal Article

A robust factor analysis model using the restricted skew-t distribution

Lin, 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

A robust factor analysis model using the restricted skew-t distribution

2014

Journal Article

On the number of components in a Gaussian mixture model

McLachlan, 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

On the number of components in a Gaussian mixture model

2014

Journal Article

Joint modeling and registration of cell populations in cohorts of high-dimensional flow cytometric data

Pyne, 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

Joint modeling and registration of cell populations in cohorts of high-dimensional flow cytometric data

2014

Journal Article

False discovery rate control in magnetic resonance imaging studies via Markov random fields

Nguyen, 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

False discovery rate control in magnetic resonance imaging studies via Markov random fields