Mixture Models

Valuable resource for researchers and postgraduate students from statistics, biostatistics, and other fields. It could be used as a textbook for a course on model-based clustering methods, and as a supplementary text for courses on data mining, semiparametric modeling, and high-dimensional data analysis.

Mixture models are a powerful tool for analyzing complex and heterogeneous datasets across many scientific fields, from finance to genomics. Mixture Models: Parametric, Semiparametric, and New Directions provides an up-to-date introduction to these models, their recent developments, and their implementation using R. It fills a gap in the literature by covering not only the basics of finite mixture models, but also recent developments such as semiparametric extensions, robust modeling, label switching, and high-dimensional modeling.

Features

• Comprehensive overview of the methods and applications of mixture models
• Key topics include hypothesis testing, model selection, estimation methods, and Bayesian approaches
• Recent developments, such as semiparametric extensions, robust modeling, label switching, and high-dimensional modeling
• Examples and case studies from such fields as astronomy, biology, genomics, economics, finance, medicine, engineering, and sociology
• Integrated R code for many of the models, with code and data available in the R Package MixSemiRob

Mixture Models: Parametric, Semiparametric, and New Directions is a valuable resource for researchers and postgraduate students from statistics, biostatistics, and other fields. It could be used as a textbook for a course on model-based clustering methods, and as a supplementary text for courses on data mining, semiparametric modeling, and high-dimensional data analysis.