Statistical Learning from a Regression Perspective

By: Richard A. Berk

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Paperback | 16 June 2018 | Edition Number 2

At a Glance

Paperback
376 Pages

Edition Type
Revised

Dimensions(cm)
23.5 x 15.5 x 1.98

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Statistical Learning from a Regression Perspective considers statistical learning applications when interest centers on the conditional distribution of the response variable, given a set of predictors, and when it is important to characterize how the predictors are related to the response. As a first approximation, this is can be seen as an extension of nonparametric regression. Among the statistical learning procedures examined are bagging, random forests, boosting, and support vector machines. Response variables may be quantitative or categorical.

Real applications are emphasized, especially those with practical implications. One important theme is the need to explicitly take into account asymmetric costs in the fitting process. For example, in some situations false positives may be far less costly than false negatives. Another important theme is to not automatically cede modeling decisions to a fitting algorithm. In many settings, subject-matter knowledge should trump formal fitting criteria. Yet another important theme is to appreciate the limitation of one's data and not apply statistical learning procedures that require more than the data can provide.

The material is written for graduate students in the social and life sciences and for researchers who want to apply statistical learning procedures to scientific and policy problems. Intuitive explanations and visual representations are prominent. All of the analyses included are done in R.

Industry Reviews

"This book is an outstanding example of synthesizing theoretical knowledge with applications, mathematical notations with R code, and statistics with machine learning. It has relevant exercise sets and will be an excellent textbook for a broad range of quantitatively oriented students, specifically, for those specializing in data science or taking a course on statistical learning." (Vyacheslav Lyubchich, Technometrics, Vol. 59 (4), November, 2017)

"The book focuses on supervised learning techniques that can be viewed as a form of regression ... . There are instructive problems at the end ... and examples with code in R to illustrate throughout. ... This is a thought provoking book worthy of serious attention by machine learning practitioners." (Peter Rabinovitch, MAA Reviews, July, 2017)

"The intended audience includes advanced undergraduate and graduate students biostatistics in the fields of social science and life science, as well as researchers who want to apply statistical learning procedures to scientific and policy problems. ... This is an excellent overview of statistical learning applications. It is strongly recommended to advanced researchers and statisticians particularly interested in the social and behavioral aspects of data analysis." (Puja Sitwala, Doody's Book Reviews, January, 2017)

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