Mixture Models Parametric Semiparametric And New Directions Weixin Yao Sijia Xiang by Weixin Yao & Sijia Xiang 9780367481827, 9781003038511, 0367481820, 1003038514 instant download after payment.

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 including 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