Name: Mixture Models: Parametric, Semiparametric, and new Directions (Chapman & Hall (en Inglés)
SKU: 56123901
Price: 104.16 USD
Availability: InStock
Author: Yao Weixin,Xiang Sijia

portada Mixture Models: Parametric, Semiparametric, and new Directions (Chapman & Hall (en Inglés)

Mixture Models: Parametric, Semiparametric, and new Directions (Chapman & Hall (en Inglés)

Formato

Libro Físico

Editorial

Crc Press

Autor

Yao Weixin,Xiang Sijia

Idioma

Inglés

N° páginas

379

Encuadernación

Tapa Dura

ISBN13

9780367481827

N° edición

Mixture Models: Parametric, Semiparametric, and new Directions (Chapman & Hall (en Inglés)

Yao Weixin,Xiang Sijia (Autor) · Crc Press · Tapa Dura

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Reseña del libro "Mixture Models: Parametric, Semiparametric, and new Directions (Chapman & Hall (en Inglés)"

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.