- 397 pages
- English
- ePUB (mobile friendly)
- Available on iOS & Android
About this book
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.
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Information
Table of contents
- Cover Page
- Half-Title Page
- Series Page
- Title Page
- Copyright Page
- Dedication Page
- Contents
- Preface
- Symbols
- Authors
- 1 Introduction to mixture models
- 2 Mixture models for discrete data
- 3 Mixture regression models
- 4 Bayesian mixture models
- 5 Label switching for mixture models
- 6 Hypothesis testing and model selection for mixture models
- 7 Robust mixture regression models
- 8 Mixture models for high-dimensional data
- 9 Semiparametric mixture models
- 10 Semiparametric mixture regression models
- Bibliography
- Index