McNicholas, Paul D. Mixture model-based classification. (English) Zbl 1454.62005 Boca Raton, FL: CRC Press (ISBN 978-1-4822-2566-2/hbk; 978-1-4822-2567-9/ebook). xxiv, 212 p. (2017). Publisher’s description: This book is the first monograph devoted to mixture model-based approaches to clustering and classification. This is both a book for established researchers and newcomers to the field. A history of mixture models as a tool for classification is provided and Gaussian mixtures are considered extensively, including mixtures of factor analyzers and other approaches for high-dimensional data. Non-Gaussian mixtures are considered, from mixtures with components that parameterize skewness and/or concentration, right up to mixtures of multiple scaled distributions. Several other important topics are considered, including mixture approaches for clustering and classification of longitudinal data as well as discussion about how to define a cluster. Cited in 25 Documents MSC: 62-02 Research exposition (monographs, survey articles) pertaining to statistics 62H30 Classification and discrimination; cluster analysis (statistical aspects) 62E10 Characterization and structure theory of statistical distributions 62H25 Factor analysis and principal components; correspondence analysis PDF BibTeX XML Cite \textit{P. D. McNicholas}, Mixture model-based classification. Boca Raton, FL: CRC Press (2017; Zbl 1454.62005) Full Text: DOI OpenURL