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2021 Book Chapter Estimation of classification rules from partially classified dataMcLachlan, Geoffrey and Ahfock, Daniel (2021). Estimation of classification rules from partially classified data. Data analysis and rationality in a complex world. (pp. 149-157) edited by Theodore Chadjipadelis, Berthold Lausen, Angelos Markos, Tae Rim Lee, Angela Montanari and Rebecca Nugent. Cham, Switzerland: Springer. doi: 10.1007/978-3-030-60104-1_17 |
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2021 Book Chapter Automated gating and dimension reduction of high-dimensional cytometry dataLee, Sharon X., McLachlan, Geoffrey J. and Pyne, Saumyadipta (2021). Automated gating and dimension reduction of high-dimensional cytometry data. Mathematical, computational and experimental T cell immunology. (pp. 281-294) edited by Carmen Molina-París and Grant Lythe . Cham, Switzerland: Springer. doi: 10.1007/978-3-030-57204-4_16 |
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2020 Book Chapter Comprehensive chemometrics: chemical and biochemical data analysisMcLachlan, G. J., Rathnayake, S. and Lee, S. X. (2020). Comprehensive chemometrics: chemical and biochemical data analysis. Comprehensive chemometrics: chemical and biochemical data analysis. (pp. 267-304) edited by Steven Brown, Roma Tauler and Beata Walczak. Oxford, United Kingdom: Elsevier. |
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2019 Book Chapter Mixture of factor analyzers for the clustering and visualization of high-dimensional dataMcLachlan, Geoffrey J., Baek, Jangsun and Rathnayake, Suren I. (2019). Mixture of factor analyzers for the clustering and visualization of high-dimensional data. Advances in latent class analysis: a festschrift in honor of C. Mitchell Dayton. (pp. 79-98) edited by Gregory R. Hancock, Jeffrey R. Harring and George B. Macready. Charlotte, NC, United States: Information Age Publishing. |
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2018 Book Chapter Risk measures based on multivariate skew normal and skew t-mixture modelsLee, Sharon X. and McLachlan, Geoffrey J. (2018). Risk measures based on multivariate skew normal and skew t-mixture models. Asymmetric dependence in finance: diversification, correlation and portfolio management in market downturns. (pp. 152-168) edited by Jamie Alcock and Stephen Satchell. Chichester, West Sussex, United Kingdom: John Wiley & Sons. doi: 10.1002/9781119288992.ch7 |
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2017 Book Chapter Statistical evaluation of labeled comparative profiling proteomics experiments using permutation testNguyen, Hien D., McLachlan, Geoffrey J. and Hill, Michelle M. (2017). Statistical evaluation of labeled comparative profiling proteomics experiments using permutation test. Proteome bioinformatics. (pp. 109-117) edited by Shivakumar Keerthikumar and Suresh Mathivanan. New York, NY United States: Humana Press. doi: 10.1007/978-1-4939-6740-7_9 |
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2017 Book Chapter Finite mixture models in biostatisticsLee, Sharon X., Ng, Shu-Kay and McLachlan, Geoffrey J. (2017). Finite mixture models in biostatistics. Disease Modelling and Public Health, Part A. (pp. 75-102) edited by Arni S.R. Srinivasa Rao, Saumyadipta Pyne and C.R. Rao. Amsterdam, Netherlands: Elsevier. doi: 10.1016/bs.host.2017.08.005 |
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2017 Book Chapter ClusteringMcLachlan, G. J., Bean, R. W. and Ng, S. K. (2017). Clustering. Bioinformatics Vol. II: Structure, Function, and Applications. (pp. 345-362) edited by Jonathan M. Keith. New York, NY, United States: Humana Press. doi: 10.1007/978-1-4939-6613-4_19 |
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2017 Book Chapter On the identification of correlated differential features for supervised classification of high-dimensional dataNg, Shu Kay and McLachlan, Geoffrey J. (2017). On the identification of correlated differential features for supervised classification of high-dimensional data. Data science, innovative developments in data analysis and clustering. (pp. 43-57) edited by Francesco Palumbo, Angela Montanari and Maurizio Vichi. Cham, Switzerland: Springer. doi: 10.1007/978-3-319-55723-6 |
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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 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 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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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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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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2013 Book Chapter Clustering of gene expression data via normal mixture modelsMcLachlan, G. J., Flack, L. K., Ng, S. K. and Wang, K. (2013). Clustering of gene expression data via normal mixture models. Statistical methods for microarray data analysis: methods and protocols. (pp. 103-119) edited by Andrei Y. Yakovlev, Lev Klebanov and Daniel Gaile. New York, NY, United States: Humana Press. doi: 10.1007/978-1-60327-337-4_7 |
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2012 Book Chapter An enduring interest in classification: supervised and unsupervisedMcLachlan, G. J. (2012). An enduring interest in classification: supervised and unsupervised. Journeys to data mining: experiences from 15 renowned researchers. (pp. 147-171) edited by Mohamed Medhat Gaber. Heidelberg, Germany: Springer. doi: 10.1007/978-3-642-28047-4_12 |
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2012 Book Chapter The EM algorithmNg, Shu Kay, Krishnan, Thriyambakam and McLachlan, Geoffrey J. (2012). The EM algorithm. Handbook of Computational Statistics: Concepts and Methods. (pp. 139-172) edited by James E. Gentle, Wolfgang Karl Hardle and Yuichi Mori. Berlin & New York: Springer. doi: 10.1007/978-3-642-21551-3__6 |
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2011 Book Chapter The EM AlgorithmNg, Shu Kay, Krishnan, Thriyambakam and McLachlan, Geoffrey J. (2011). The EM Algorithm. Handbook of Computational Statistics. (pp. 139-172) Berlin, Germany: Springer. doi: 10.1007/978-3-642-21551-3_6 |
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2011 Book Chapter Mixtures of factor analyzers for the analysis of high-dimensional dataMcLachlan, Geoffrey J., Baek, Jangsun and Rathnayake, Suren I. (2011). Mixtures of factor analyzers for the analysis of high-dimensional data. Mixture estimation and applications. (pp. 189-212) edited by Kerrie L. Mengersen, Christian P. Robert and D. Michael Titterington. Chichester, United Kingdom: John Wiley and Sons. doi: 10.1002/9781119995678.ch9 |
