1st Edition

Mixture Model-Based Classification

By Paul D. McNicholas Copyright 2017
236 Pages
by Chapman & Hall

236 Pages 38 B/W Illustrations
by Chapman & Hall

236 Pages 38 B/W Illustrations
by Chapman & Hall

"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... Read more

Mixture Model-Based Classification

Biography

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.

"This Monograph, “Mixture Model-Based Classification” is an excellent book, highly relevant to every statistician working with classification problems."
~International Society for Clinical Biostatistics

 "This monograph is an extensive introduction of mixture models with applications in classification and clustering. . . The author did good work by organizing the materials in a very natural way as well as presenting methods and algorithms in great detail. Moreover, many case studies help the reader understand and appreciate the methodologies presented."
~Journal of the American Statistical Association

"I would recommend this book to anyone interested in learning about application of mixture models to classification problems."
~The International Biometric Society