Statistical Foundations of Data Science (Hardcover)

Statistical Foundations of Data Science gives a thorough introduction to commonly used statistical models, contemporary statistical machine learning techniques and algorithms, along with their mathematical insights and statistical theories. It aims to serve as a graduate-level textbook and a research monograph on high-dimensional statistics, sparsity and covariance learning, machine learning, and statistical inference. It includes ample exercises that involve both theoretical studies as well as empirical applications.

The book begins with an introduction to the stylized features of big data and their impacts on statistical analysis. It then introduces multiple linear regression and expands the techniques of model building via nonparametric regression and kernel tricks. It provides a comprehensive account on sparsity explorations and model selections for multiple regression, generalized linear models, quantile regression, robust regression, hazards regression, among others. High-dimensional inference is also thoroughly addressed and so is feature screening. The book also provides a comprehensive account on high-dimensional covariance estimation, learning latent factors and hidden structures, as well as their applications to statistical estimation, inference, prediction and machine learning problems. It also introduces thoroughly statistical machine learning theory and methods for classification, clustering, and prediction. These include CART, random forests, boosting, support vector machines, clustering algorithms, sparse PCA, and deep learning.

《資料科學的統計基礎》是一本全面介紹常用統計模型、現代統計機器學習技術和算法的書籍，並提供相關的數學洞察和統計理論。它旨在作為研究生教材和高維統計、稀疏性和協方差學習、機器學習和統計推斷的研究專著。書中包含豐富的練習，既涉及理論研究，也包括實際應用。

本書首先介紹了大數據的特點及其對統計分析的影響。然後通過多元線性回歸引入模型構建的技巧，包括非參數回歸和核技巧。它全面介紹了稀疏性探索和多元回歸、廣義線性模型、分位數回歸、魯棒回歸、風險回歸等模型選擇的方法。高維推斷和特徵篩選也得到了徹底的討論。本書還全面介紹了高維協方差估計、學習潛在因子和隱藏結構，以及它們在統計估計、推斷、預測和機器學習問題中的應用。它還徹底介紹了用於分類、聚類和預測的統計機器學習理論和方法，包括CART、隨機森林、提升、支持向量機、聚類算法、稀疏主成分分析和深度學習。

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The authors are international authorities and leaders on the presented topics. All are fellows of the Institute of Mathematical Statistics and the American Statistical Association.

Jianqing Fan is Frederick L. Moore Professor, Princeton University. He is co-editing Journal of Business and Economics Statistics and was the co-editor of The Annals of Statistics, Probability Theory and Related Fields, and Journal of Econometrics and has been recognized by the 2000 COPSS Presidents' Award, AAAS Fellow, Guggenheim Fellow, Guy medal in silver, Noether Senior Scholar Award, and Academician of Academia Sinica.

Runze Li is Elberly family chair professor and AAAS fellow, Pennsylvania State University, and was co-editor of The Annals of Statistics.

Cun-Hui Zhang is distinguished professor, Rutgers University and was co-editor of Statistical Science.

Hui Zou is professor, University of Minnesota and was action editor of Journal of Machine Learning Research.

作者是所介紹主題的國際權威和領導者。他們都是數理統計學會和美國統計學會的會士。

Jianqing Fan 是普林斯頓大學的Frederick L. Moore教授。他正在共同編輯《商業與經濟統計學期刊》，並曾擔任《統計學年鑑》、《概率論與相關領域》和《計量經濟學期刊》的共同編輯。他曾獲得2000年COPSS主席獎、AAAS會士、古根漢獎學金、銀牌Guy獎章、Noether高級學者獎和中央研究院院士的認可。

Runze Li 是賓夕法尼亞州立大學的Elberly家族講座教授和AAAS會士，曾擔任《統計學年鑑》的共同編輯。

Cun-Hui Zhang 是羅格斯大學的傑出教授，曾擔任《統計科學》的共同編輯。

Hui Zou 是明尼蘇達大學的教授，曾擔任《機器學習研究期刊》的行動編輯。