Part I. Classic Statistical Inference: 1. Algorithms and inference; 2. Frequentist inference; 3. Bayesian inference; 4. Fisherian inference and maximum likelihood estimation; 5. Parametric models and exponential families; Part II. Early Computer-Age Methods: 6. Empirical Bayes; 7. James–Stein estimation and ridge regression; 8. Generalized linear models and regression trees; 9. Survival analysis and the EM algorithm; 10. The jackknife and the bootstrap; 11. Bootstrap confidence intervals; 12. Cross-validation and Cp estimates of prediction error; 13. Objective Bayes inference and Markov chain Monte Carlo; 14. Statistical inference and methodology in the postwar era; Part III. Twenty-First Century Topics: 15. Large-scale hypothesis testing and false discovery rates; 16. Sparse modeling and the lasso; 17. Random forests and boosting; 18. Neural networks and deep learning; 19. Support-vector machines and kernel methods; 20. Inference after model selection; 21. Empirical Bayes estimation strategies; Epilogue; References; Index.
Take an exhilarating journey through the modern revolution in statistics with two of the ringleaders.
Bradley Efron is Max H. Stein Professor, Professor of Statistics, and Professor of Biomedical Data Science at Stanford University, California. He has held visiting faculty appointments at Harvard University, Massachusetts, the University of California, Berkeley, and Imperial College of Science, Technology and Medicine, 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, California. He is coauthor of Elements of Statistical Learning, a key text in the field 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.
'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
'Absolutely brilliant. This beautifully written compendium reviews
many big statistical ideas, including the authors' own. A must for
anyone engaged creatively in statistics and the data sciences, for
repeated use. Efron and Hastie demonstrate the ever-growing power
of statistical reasoning, past, present, and future.' Carl Morris,
Harvard University, Massachusetts
'Computer Age Statistical Inference gives a lucid guide to modern
statistical inference for estimation, hypothesis testing, and
prediction. The book seamlessly integrates statistical thinking
with computational thinking, while covering a broad range of
powerful algorithms for learning from data. It is extraordinarily
rare and valuable to have such a unified treatment of classical
(and classic) statistical ideas and recent 'big data' and machine
learning ideas. Accessible real-world examples and insightful
remarks can be found throughout the book.' Joseph K. Blitzstein,
Harvard University, Massachusetts
'Among other things, it is an attempt to characterize the current
state of statistics by identifying important tools in the context
of their historical development. It also offers an enlightening
series of illustrations of the interplay between computation and
inference … This is an attractive book that invites browsing by
anyone interested in statistics and its future directions.' Bill
Satzer, Mathematical Association of America Reviews
'My take on Computer Age Statistical Inference is that experienced
statisticians will find it helpful to have such a compact summary
of twentieth-century statistics, even if they occasionally disagree
with the book's emphasis; students beginning the study of
statistics will value the book as a guide to statistical inference
that may offset the dangerously mind-numbing experience offered by
most introductory statistics textbooks; and the rest of us
non-experts interested in the details will enjoy hundreds of hours
of pleasurable reading.' Joseph Rickert, RStudio
(www.rstudio.com)
'Efron and Hastie (both, Stanford Univ.) have superbly crafted a
central text/reference book that presents a broad overview of
modern statistics. The work examines major developments in
computation from the late-20th and early-21st centuries, ranging
from electronic computations to 'big data' analysis. Focusing
primarily on the last six decades, the text thoroughly documents
the progression within the discipline of statistics … This text is
highly recommended for graduate libraries.' D. J. Gougeon, Choice
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