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James · Witten · Hastie · Tibshirani
Springer Texts in Statistics
Gareth James · Daniela Witten · Trevor Hastie · Robert Tibshirani
An Introduction to Statistical Learning
with Applications in R
Springer Texts in Statistics
An Introduction
to Statistical
Learning
Gareth James
Daniela Witten
Trevor Hastie
Robert Tibshirani
Statistics
An Introduction to Statistical Learning
with Applications in R
An Introduction to Statistical Learning provides an accessible overview of the fi eld
of statistical learning, an essential toolset for making sense of the vast and complex
data sets that have emerged in fi elds ranging from biology to fi nance to marketing to
astrophysics in the past twenty years. is book presents some of the most important
modeling and prediction techniques, along with relevant applications. Topics include
linear regression, classifi cation, resampling methods, shrinkage approaches, tree-based
methods, support vector machines, clustering, and more. Color graphics and real-world
examples are used to illustrate the methods presented. Since the goal of this textbook
is to facilitate the use of these statistical learning techniques by practitioners in sci-
ence, industry, and other fi elds, each chapter contains a tutorial on implementing the
analyses and methods presented in R, an extremely popular open source statistical
so ware platform.
Two of the authors co-wrote e Elements of Statistical Learning (Hastie, Tibshirani
and Friedman, 2nd edition 2009), a popular reference book for statistics and machine
learning researchers. An Introduction to Statistical Learning covers many of the same
topics, but at a level accessible to a much broader audience. is book is targeted at
statisticians and non-statisticians alike who wish to use cutting-edge statistical learn-
ing techniques to analyze their data. e text assumes only a previous course in linear
regression and no knowledge of matrix algebra.
Gareth James is a professor of statistics at University of Southern California. He has
published an extensive body of methodological work in the domain of statistical learn-
ing with particular emphasis on high-dimensional and functional data. e conceptual
framework for this book grew out of his MBA elective courses in this area.
Daniela Witten is an assistant professor of biostatistics at University of Washington. Her
research focuses largely on high-dimensional statistical machine learning. She has
contributed to the translation of statistical learning techniques to the fi eld of genomics,
through collaborations and as a member of the Institute of Medicine committee that
led to the report Evolution of Translational Omics.
Trevor Hastie and Robert Tibshirani are professors of statistics at Stanford University, and
are co-authors of the successful textbook Elements of Statistical Learning. Hastie and
Tibshirani developed generalized additive models and wrote a popular book of that
title. Hastie co-developed much of the statistical modeling so ware and environment
in R/S-PLUS and invented principal curves and surfaces. Tibshirani proposed the lasso
and is co-author of the very successful An Introduction to the Bootstrap.
9 781461 471370
ISBN 978-1-4614-7137-0
STS
