Mixture Models: Parametric, Semiparametric, and New Directions
暫譯: 混合模型：參數型、半參數型及新方向
Yao, Weixin, Xiang, Sijia
- 出版商: CRC
- 出版日期: 2024-04-18
- 售價: $4,100
- 貴賓價: 9.5 折 $3,895
- 語言: 英文
- 頁數: 379
- 裝訂: Hardcover - also called cloth, retail trade, or trade
- ISBN: 0367481820
- ISBN-13: 9780367481827
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相關分類:
R 語言、Data-mining
海外代購書籍(需單獨結帳)
相關主題
商品描述
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.
商品描述(中文翻譯)
混合模型是分析複雜且異質數據集的強大工具，應用於許多科學領域，從金融到基因組學。《混合模型：參數型、半參數型及新方向》提供了這些模型的最新介紹、近期發展及其在 R 語言中的實現。它填補了文獻中的空白，不僅涵蓋了有限混合模型的基本知識，還包括最近的發展，如半參數擴展、穩健建模、標籤切換和高維建模。
特點：
- 混合模型方法和應用的全面概述
- 主要主題包括假設檢驗、模型選擇、估計方法和貝葉斯方法
- 最近的發展，如半參數擴展、穩健建模、標籤切換和高維建模
- 來自天文學、生物學、基因組學、經濟學、金融、醫學、工程學和社會學等領域的範例和案例研究
- 許多模型的整合 R 代碼，代碼和數據可在 R 套件 MixSemiRob 中獲得
《混合模型：參數型、半參數型及新方向》是統計學、生物統計學及其他領域研究人員和研究生的寶貴資源。它可以作為模型基礎聚類方法課程的教科書，以及數據挖掘、半參數建模和高維數據分析課程的補充教材。
作者簡介
Dr. Weixin Yao is professor and vice chair of the Department of Statistics at the University of California, Riverside. He received his BS in statistics from the University of Science and Technology of China in 2002 and his PhD in statistics from Pennsylvania State University in 2007. His major research includes mixture models, nonparametric and semiparametric modeling, robust data analysis, and high-dimensional modeling. He has served as an associate editor for Biometrics, Journal of Computational and Graphical Statistics, Journal of Multivariate Analysis, and The American Statistician. In addition, Dr. Yao was also the guest editor of Advances in Data Analysis and Classification for the special issue on "Models and Learning for Clustering and Classification," 2020-2021.
Dr. Sijia Xiang is a professor in statistics. She obtained her doctoral and master's degrees in statistics from Kansas State University in 2014 and 2012, respectively. Her research interests include mixture models, nonparametric/semiparametric estimation, robust estimation, and dimension reduction. Dr. Xiang has led several research projects, including, "Statistical inference for clustering analysis based on high-dimensional mixture models," funded by the National Social Science Fund of China, "Semiparametric mixture model and variable selection research," funded by the National Natural Science Foundation of China, and "Research on the new estimation method and application of mixture model," funded by the Zhejiang Statistical Research Project. Dr. Xiang has also been selected as a Young Discipline Leader and a Young Talented Person in the Zhejiang Provincial University Leadership Program.
Dr. Xiang has published extensively in international journals, including Annals of the Institute of Statistical Mathematics, Statistical Science, Journal of Statistical Planning and Inference, and more. Her research mainly focuses on semiparametric mixture models, which include semiparametric mixtures of regressions with single-index for model-based clustering, semiparametric mixtures of nonparametric regressions, and continuous scale mixture approaches. Dr. Xiang has also contributed to the development of new estimation methods for mixtures of linear regression models and mixtures of factor analyzers. Additionally, she has proposed a new bandwidth selection method for nonparametric regressions and robust maximum Lq-likelihood estimation for joint mean-covariance models for longitudinal data.
作者簡介(中文翻譯)
姚維新博士是加州大學河濱分校統計系的教授及副系主任。他於2002年獲得中國科學技術大學的統計學學士學位，並於2007年獲得賓夕法尼亞州立大學的統計學博士學位。他的主要研究包括混合模型、非參數及半參數建模、穩健數據分析和高維建模。他曾擔任Biometrics、Journal of Computational and Graphical Statistics、Journal of Multivariate Analysis及The American Statistician的副編輯。此外，姚博士還擔任了Advances in Data Analysis and Classification的特刊客座編輯，該特刊主題為「聚類與分類的模型與學習」，時間為2020-2021年。
向思佳博士是統計學教授。她於2014年和2012年分別在堪薩斯州立大學獲得統計學博士及碩士學位。她的研究興趣包括混合模型、非參數/半參數估計、穩健估計和降維。向博士主導了幾個研究項目，包括「基於高維混合模型的聚類分析統計推斷」，該項目由中國國家社會科學基金資助；「半參數混合模型及變數選擇研究」，由中國國家自然科學基金資助；以及「混合模型的新估計方法及應用研究」，由浙江省統計研究項目資助。向博士還被選為浙江省大學領導計劃的青年學科帶頭人和青年人才。
向博士在國際期刊上發表了大量論文，包括Annals of the Institute of Statistical Mathematics、Statistical Science、Journal of Statistical Planning and Inference等。她的研究主要集中在半參數混合模型上，包括用於基於模型的聚類的單指數半參數回歸混合、非參數回歸的半參數混合以及連續尺度混合方法。向博士還為線性回歸模型的混合和因子分析器的混合開發了新的估計方法。此外，她還提出了一種新的非參數回歸帶寬選擇方法，以及針對縱向數據的聯合均值-協方差模型的穩健最大Lq-似然估計。
