The twenty-rst century has seen a
breathtaking expansion of statistical
methodology, both in scope and in
inuence. “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 inuential 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.
Efron & hastiE
ComputEr agE
statistiCal infErEnCE
“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 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
“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
“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.”
— 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, author of Seven Pillars of Statistical Wisdom
“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, The University of Chicago Booth School of Business
Bradley Efron is Max H. Stein Professor,
Professor of Statistics, and Professor of
Biomedical Data Science at Stanford University.
He has held visiting faculty appointments at
Harvard, UC Berkeley, and Imperial College
London. Efron has worked extensively on
theories of statistical inference, and is the
inventor of the bootstrap sampling technique.
He received the National Medal of Science in
2005 and the Guy Medal in Gold of the Royal
Statistical Society in 2014.
Trevor Hastie is John A. Overdeck Professor,
Professor of Statistics, and Professor of
Biomedical Data Science at Stanford University.
He is coauthor of Elements of Statistical
Learning, a key text in the eld of modern
data analysis. He is also known for his work
on generalized additive models and principal
curves, and for his contributions to the R
computing environment. Hastie was awarded
the Emmanuel and Carol Parzen prize for
Statistical Innovation in 2014.
Institute of Mathematical Statistics
Monographs
Editorial Board:
D. R. Cox (University of Oxford)
B. Hambly (University of Oxford)
S. Holmes (Stanford University)
J. Wellner (University of Washington)
Cover illustration: Pacific Ocean wave, North Shore, Oahu,
Hawaii. © Brian Sytnyk / Getty Images.
Cover designed by Zoe Naylor.
PRINTED IN THE UNITED KINGDOM
ComputEr agE
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algorithms, EvidEnCE, and data sCiEnCE
BradlEy Efron
trEvor hastiE
9781107149892 Efron & Hastie JKT C M Y K
The Work, Computer Age Statistical Inference, was first published by Cambridge University Press.
c
in the Work, Bradley Efron and Trevor Hastie, 2016.
Cambridge University Press’s catalogue entry for the Work can be found at http: // www. cambridge. org/
9781107149892
NB: The copy of the Work, as displayed on this website, can be purchased through Cambridge University
Press and other standard distribution channels. This copy is made available for personal use only and must
not be adapted, sold or re-distributed.
Corrected November 10, 2017.