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>