Sparse Graphical Modeling for High Dimensional Data: A Paradigm of Conditional Independence Tests
暫譯: 高維數據的稀疏圖形建模：條件獨立性測試的範式
Liang, Faming, Jia, Bochao
- 出版商: CRC
- 出版日期: 2023-08-02
- 售價: $4,200
- 貴賓價: 9.5 折 $3,990
- 語言: 英文
- 頁數: 130
- 裝訂: Hardcover - also called cloth, retail trade, or trade
- ISBN: 0367183730
- ISBN-13: 9780367183738
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相關分類:
大數據 Big-data、機率統計學 Probability-and-statistics
海外代購書籍(需單獨結帳)
商品描述
This book provides a general framework for learning sparse graphical models with conditional independence tests. It includes complete treatments for Gaussian, Poisson, multinomial, and mixed data; unified treatments for covariate adjustments, data integration, and network comparison; unified treatments for missing data and heterogeneous data; efficient methods for joint estimation of multiple graphical models; effective methods of high-dimensional variable selection; and effective methods of high-dimensional inference. The methods possess an embarrassingly parallel structure in performing conditional independence tests, and the computation can be significantly accelerated by running in parallel on a multi-core computer or a parallel architecture. This book is intended to serve researchers and scientists interested in high-dimensional statistics, and graduate students in broad data science disciplines.
Key Features:
- A general framework for learning sparse graphical models with conditional independence tests
- Complete treatments for different types of data, Gaussian, Poisson, multinomial, and mixed data
- Unified treatments for data integration, network comparison, and covariate adjustment
- Unified treatments for missing data and heterogeneous data
- Efficient methods for joint estimation of multiple graphical models
- Effective methods of high-dimensional variable selection
- Effective methods of high-dimensional inference
商品描述(中文翻譯)
這本書提供了一個學習稀疏圖形模型的通用框架，並包含條件獨立性測試。它對高斯（Gaussian）、泊松（Poisson）、多項式（multinomial）和混合數據進行了完整的處理；對協變數調整、數據整合和網絡比較進行了統一的處理；對缺失數據和異質數據進行了統一的處理；提供了多個圖形模型的聯合估計的高效方法；有效的高維變數選擇方法；以及有效的高維推斷方法。這些方法在執行條件獨立性測試時具有明顯的並行結構，計算可以通過在多核計算機或並行架構上並行運行來顯著加速。本書旨在服務於對高維統計感興趣的研究人員和科學家，以及廣泛數據科學學科的研究生。
主要特點：
- 一個學習稀疏圖形模型的通用框架，並包含條件獨立性測試
- 對不同類型數據（高斯、泊松、多項式和混合數據）進行完整的處理
- 對數據整合、網絡比較和協變數調整進行統一的處理
- 對缺失數據和異質數據進行統一的處理
- 多個圖形模型的聯合估計的高效方法
- 高維變數選擇的有效方法
- 高維推斷的有效方法
作者簡介
Dr. Faming Liang is Distinguished Professor of Statistics, Purdue University. Prior joining Purdue University in 2017, he held regular faculty positions in the Department of Biostatistics, University of Florida and Department of Statistics, Texas A&M University. Dr. Liang obtained his PhD degree from the Chinese University of Hong Kong in 1997. Dr. Liang is ASA fellow, IMS fellow, and elected member of International Statistical Association. Dr. Liang is also a winner of Youden Prize 2017. Dr. Liang has served as co-editor for Journal of Computational and Graphical Statistics, associate editor for multiple statistical journals, including Journal of the American Statistical Association, Journal of Computational and Graphical Statistics, Technometrics, Bayesian Analysis, and Biometrics, and editorial board member for Nature Scientific Report. Dr. Liang has published two books and over 130 journal/conference papers, which involve a variety of research fields such as Markov chain Monte Carlo, machine learning, bioinformatics, high-dimensional statistics, and big data computing.
Dr. Bochao Jia is research scientist at Eli Lilly and Company, Lilly Corporate Center, Indianapolis, Indiana, U.S.A. Dr. Jia obtained his PhD degree from University of Florida in 2018. Dr. Jia has published quite a few papers on sparse graphical modelling.
作者簡介(中文翻譯)
梁發明博士是普渡大學的統計學特聘教授。在2017年加入普渡大學之前，他曾在佛羅里達大學的生物統計學系和德克薩斯農工大學的統計學系擔任教職。梁博士於1997年獲得香港中文大學的博士學位。梁博士是美國統計協會（ASA）會士、國際數理統計學會（IMS）會士，並且是國際統計學會的當選成員。梁博士也是2017年Youden獎的得主。梁博士曾擔任計算與圖形統計期刊的共同編輯，並擔任多個統計期刊的副編輯，包括美國統計協會期刊、計算與圖形統計期刊、技術計量學、貝葉斯分析和生物統計學，以及自然科學報告的編輯委員會成員。梁博士已出版兩本書籍和超過130篇期刊/會議論文，涉及馬可夫鏈蒙地卡羅、機器學習、生物資訊學、高維統計學和大數據計算等多個研究領域。
賈博超博士是美國印第安納州印第安納波利斯的禮來公司（Eli Lilly and Company）研究科學家。賈博士於2018年獲得佛羅里達大學的博士學位。賈博士在稀疏圖形建模方面發表了相當多的論文。
