Mixture Models : Parametric, Semiparametric, and New Directions

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--

• | Author: Weixin Yao, Sijia Xiang
• | Publisher: Chapman & Hall/CRC
• | Publication Date: Apr 18, 2024
• | Number of Pages: NA pages
• | Language: English
• | Binding: Hardcover
• | ISBN-10: 0367481820
• | ISBN-13: 9780367481827