Mixture Models

<p>Mixture models are a powerful tool for analyzing complex and heterogeneous datasets across many scientific fields, from finance to genomics. <b>Mixture Models: Parametric, Semiparametric, and New Directions</b> 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.</p><p> <b>Features</b></p><ul><li>Comprehensive overview of the methods and applications of mixture models</li><li>Key topics include hypothesis testing, model selection, estimation methods, and Bayesian approaches</li><li>Recent developments, such as semiparametric extensions, robust modeling, label switching, and high-dimensional modeling</li><li>Examples and case studies from such fields as astronomy, biology, genomics, economics, finance, medicine, engineering, and sociology</li><li>Integrated R code for many of the models, with code and data available in the R Package MixSemiRob</li></ul><p><b>Mixture Models: Parametric, Semiparametric, and New Directions</b> 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.</p><p> </p>