“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 effec-
tiveness of statistics and machine learning in the context of a series of clear, historically
informed examples.”
— Andrew Gelman, Columbia University
“This unusual book describes the nature of statistics by displaying multiple examples
of the way the field has evolved over the past 60 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 espe-
cially 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.”
—RobKass,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 compu-
tational 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, author of
Seven Pillars of Statistical Wisdom
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