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Mixture Model-Based Classification (Paperback)
Loot Price: R1,383
Discovery Miles 13 830
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Mixture Model-Based Classification (Paperback)
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"This is a great overview of the field of model-based clustering
and classification by one of its leading developers. McNicholas
provides a resource that I am certain will be used by researchers
in statistics and related disciplines for quite some time. The
discussion of mixtures with heavy tails and asymmetric
distributions will place this text as the authoritative, modern
reference in the mixture modeling literature." (Douglas Steinley,
University of Missouri) Mixture Model-Based Classification 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 Paul D. McNicholas is the Canada Research Chair
in Computational Statistics at McMaster University, where he is a
Professor in the Department of Mathematics and Statistics. His
research focuses on the use of mixture model-based approaches for
classification, with particular attention to clustering
applications, and he has published extensively within the field. He
is an associate editor for several journals and has served as a
guest editor for a number of special issues on mixture models.
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