- Series: Institute of Mathematical Statistics Monographs (Book 5)
- Hardcover: 495 pages
- Publisher: Cambridge University Press; 1 edition (July 21, 2016)
- Language: English
- ISBN-10: 1107149894
- ISBN-13: 978-1107149892
- Product Dimensions: 6 x 1.1 x 9 inches
- Shipping Weight: 2.1 pounds (View shipping rates and policies)
- Average Customer Review: 11 customer reviews
- Amazon Best Sellers Rank: #61,326 in Books (See Top 100 in Books)
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Computer Age Statistical Inference: Algorithms, Evidence, and Data Science (Institute of Mathematical Statistics Monographs) 1st Edition
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"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 inference-prediction 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 twenty-first 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 two-and-a-half-century 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 mid-level overview of the last 60-some years by detailing the nuances of a statistical community that, historically, has been self-segregated 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 high-speed 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
Computing 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 large-scale prediction algorithms of today. Anyone who applies statistical methods to data will benefit from this landmark text.
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An Introduction to the Bootstrap (Chapman & Hall/CRC Monographs on Statistics & Applied Probability)
Large-Scale Inference: Empirical Bayes Methods for Estimation, Testing, and Prediction (Institute of Mathematical Statistics Monographs)
The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)
Statistical Learning with Sparsity: The Lasso and Generalizations (Chapman & Hall/CRC Monographs on Statistics & Applied Probability)
Roughly 50% of the book consists of abridged topics from the above. In particular, most of part III is machine learning topics that are presented better in Hastie's books. The deep learning chapter is just too brief to be useful. I with they cut these out and only included new insights.
Part I and some chapters from II and III are new. The comments about frequentist and Bayesian inference are illustrative. Same for James-Stein estimator. I was hoping for an update on bootstrap, but it's the same material, just with new presentation and simpler examples. All of this makes reading the book worthwhile.
The level of difficulty is similar to their other books. There are many examples and illustrations, but more could have been included for Bayesian multiple testing.
It's very up-to-date and a great reference book for both intro-level students and statistics professionals.
The explanations of concepts are vivid and easy to understand, and quite often it makes you think from a different angle. Love the writing style!
It's an academic book, but a quite accessible, insightful and pleasant read.
I say mathematically sophisticated because the book is full of equations, derivations and theorems. The authors state that their intended audience is beginning graduate students and they have matched that intended level of mathematical expertise. The book is not then for those from a non-STEM background desiring a better understanding of these methods.
If I had to make a criticism it is that the authors cover so much of the landscape of statistical inference they are forced to be rather terse. Because of this, I had difficulty understanding those techniques where I didn't have any prior experience. But that will vary from reader to reader depending on their statistical expertise. Some may find the brief summaries not sophisticated enough.
In short, Computer Age Statistical Inference does a masterful job of linking the traditional inference techniques of Fisher and Neyman to modern machine learning all the while showing their similarities and differences. For those working in these disciplines and wanting to have a mathematically grounded understanding of the wide variety of methods now available for statistical inference this book is a much needed guide.
Most recent customer reviews
Both macro views and micro analysis.