We are pleased to introduce the latest in the popular IMS Monographs series, published in a cooperative arrangement with Cambridge University Press. Computer Age Statistical Inference: Algorithms, Evidence, and Data Science is written by Bradley Efron and Trevor Hastie, both from Stanford University. Published in the UK in July and the USA in September, you can get your copy (with your 40% IMS member’s discount) from www.cambridge.org/ims

If you’re going to JSM you can pick up a copy there: Brad and Trevor will be doing a book signing at the Cambridge University Press stand in the Expo Hall on Tuesday, August 2 at 4pm.

The 21st century has seen a breathtaking expansion of statistical methodology, both in scope and 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.

Hardback ISBN 9781107149892: US$74.99 IMS members$44.99

Some testimonials:
“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

“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

“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.”