• Title: Computer Age Statistical Inference: Algorithms, Evidence, and Data Science
• Author(s) Bradley Efron and Trevor Hastie
• Publisher: Cambridge University Press; 1 edition (July 21, 2016); eBook (Stanford University 2016)
• Hardcover: 495 pages
• eBook: PDF (493 pages)
• Language: English
• ISBN-10: 1107149894
• ISBN-13: 978-1107149892
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Book Description

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. Every aspiring data scientist should carefully study this book, use it as a reference, and carry it with them everywhere.

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.

• Bradley Efron is Max H. Stein Professor, Professor of Statistics, and Professor of Biomedical Data Science at Stanford University, California.
• Trevor Hastie is John A. Overdeck Professor, Professor of Statistics, and Professor of Biomedical Data Science at Stanford University, California. He is coauthor of Elements of Statistical Learning, a key text in the field of modern data analysis.

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