pdf Computer Age Statistical Inference: Algorithms, Evidence, and Data Science (Institute of Mathematical Statistics Monographs), 2016 by Bradley Efron and Trevor Hastie
当代统计学的两位大师，斯坦福教授Bradley Efron和Trevor Hastie出了新书《计算机时代下的统计推断：算法，证据和数据科学》，内容是关于计算机时代下的大数据统计推断，Efron出的书每本都是经典。 The twenty first century has seen a breathtaking expansion of statistical methodology, both in scope and in influence. 'Big data', 'data science', and 'machine learning' have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? This book takes us on an exhilarating journey through the revolution in data analysis following the introduction of electronic computation in the 1950s. Beginning with classical inferential theories  Bayesian, frequentist, Fisherian  individual chapters take up a series of influential topics: survival analysis, logistic regression, empirical Bayes, the jackknife and bootstrap, random forests, neural networks, Markov chain Monte Carlo, inference after model selection, and dozens more. The distinctly modern approach integrates methodology and algorithms with statistical inference. The book ends with speculation on the future direction of statistics and data science.二十一世纪以来的统计方法以一个惊人的速度扩张，无论是在范围和影响力。 “大数据”，“数据科学”和“机器学习”已成为新闻熟悉的术语，现代科学和商业带来的巨大数据集促使统计方法的发展。我们怎么会在这里？而我们要去哪里？继20世纪50年代引入的电子计算机，通过革命的数据分析，这本书让我们在一个令人振奋的旅程中。 以古典统计推理理论开始，这包括贝叶斯学派，频率论，费雪学派，后面独立的章节采取了一系列有影响力的主题：生存分析，logistic回归，经验贝叶斯，刀切法和自助法，随机森林，神经网络，马尔可夫链蒙特卡罗，模型选择后的推断，等等方法。在不同的现代方法中，集成了统计推断的方法和算法。本书以统计和数据科学的未来发展方向的猜测结束。
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Review"How and why is computational statistics taking over the world? In this serious work of synthesis that is also fun to read, Efron and Hastie, two pioneers in the integration of parametric and nonparametric statistical ideas, give their take on the unreasonable effectiveness of statistics and machine learning in the context of a series of clear, historically informed examples."
Andrew Gelman, Columbia University, New York
"This unusual book describes the nature of statistics by displaying multiple examples of the way the field has evolved over the past sixty years, as it has adapted to the rapid increase in available computing power. The authors' perspective is summarized nicely when they say, 'very roughly speaking, algorithms are what statisticians do, while inference says why they do them'. The book explains this 'why'; that is, it explains the purpose and progress of statistical research, through a close look at many major methods, methods the authors themselves have advanced and studied at great length. Both enjoyable and enlightening, Computer Age Statistical Inference is written especially for those who want to hear the big ideas, and see them instantiated through the essential mathematics that defines statistical analysis. It makes a great supplement to the traditional curricula for beginning graduate students."
Rob Kass, Carnegie Mellon University, Pennsylvania
"This is a terrific book. It gives a clear, accessible, and entertaining account of the interplay between theory and methodological development that has driven statistics in the computer age. The authors succeed brilliantly in locating contemporary algorithmic methodologies for analysis of 'big data' within the framework of established statistical theory."
Alastair Young, Imperial College London
"This is a guided tour of modern statistics that emphasizes the conceptual and computational advances of the last century. Authored by two masters of the field, it offers just the right mix of mathematical analysis and insightful commentary."
Hal Varian, Google
"Efron and Hastie guide us through the maze of breakthrough statistical methodologies following the computing evolution: why they were developed, their properties, and how they are used. Highlighting their origins, the book helps us understand each method's roles in inference and/or prediction. The inferenceprediction distinction maintained throughout the book is a welcome and important novelty in the landscape of statistics books."
Galit Shmueli, National Tsing Hua University
"A masterful guide to how the inferential bases of classical statistics can provide a principled disciplinary frame for the data science of the twentyfirst century."
Stephen Stigler, University of Chicago, and author of Seven Pillars of Statistical Wisdom
"Computer Age Statistical Inference offers a refreshing view of modern statistics. Algorithmics are put on equal footing with intuition, properties, and the abstract arguments behind them. The methods covered are indispensable to practicing statistical analysts in today's big data and big computing landscape."
Robert Gramacy, University of Chicago Booth School of Business
"Every aspiring data scientist should carefully study this book, use it as a reference, and carry it with them everywhere. The presentation through the twoandahalfcentury history of statistical inference provides insight into the development of the discipline, putting data science in its historical place."
Mark Girolami, Imperial College London
"Efron and Hastie are two immensely talented and accomplished scholars who have managed to brilliantly weave the fiber of 250 years of statistical inference into the more recent historical mechanization of computing. This book provides the reader with a midlevel overview of the last 60some years by detailing the nuances of a statistical community that, historically, has been selfsegregated into camps of Bayes, frequentist, and Fisher yet in more recent years has been unified by advances in computing. What is left to be explored is the emergence of, and role that, big data theory will have in bridging the gap between data science and statistical methodology. Whatever the outcome, the authors provide a vision of highspeed computing having tremendous potential to enable the contributions of statistical inference toward methodologies that address both global and societal issues."
Rebecca Doerge, Carnegie Mellon University, Pennsylvania
"In this book, two masters of modern statistics give an insightful tour of the intertwined worlds of statistics and computation. Through a series of important topics, Efron and Hastie illuminate how modern methods for predicting and understanding data are rooted in both statistical and computational thinking. They show how the rise of computational power has transformed traditional methods and questions, and how it has pointed us to new ways of thinking about statistics."
David Blei, Columbia University, New York
"Absolutely brilliant. This beautifully written compendium reviews many big statistical ideas, including the authors' own. A must for anyone engaged creatively in statistics and the data sciences, for repeated use. Efron and Hastie demonstrate the evergrowing power of statistical reasoning, past, present, and future."
Carl Morris, Harvard University, Massachusetts
"Computer Age Statistical Inference gives a lucid guide to modern statistical inference for estimation, hypothesis testing, and prediction. The book seamlessly integrates statistical thinking with computational thinking, while covering a broad range of powerful algorithms for learning from data. It is extraordinarily rare and valuable to have such a unified treatment of classical (and classic) statistical ideas and recent 'big data' and machine learning ideas. Accessible realworld examples and insightful remarks can be found throughout the book."
Joseph K. Blitzstein, Harvard University, Massachusetts
Book DescriptionComputing power has revolutionized the theory and practice of statistical inference. This book delivers a concentrated course in modern statistical thinking by tracking the revolution from classical theories to the largescale prediction algorithms of today. Anyone who applies statistical methods to data will benefit from this landmark text.
