Originally published in 1990, the first edition of Subset Selection in Regression filled a significant gap in the literature, and its critical and popular success has continued for more than a decade. Thoroughly revised to reflect progress in theory, methods, and computing power, the second edition promises to continue that tradition. The author has thoroughly updated each chapter, incorporated new material on recent developments, and included more examples and references.
New in the Second Edition:
A separate chapter on Bayesian methods
Complete revision of the chapter on estimation
A major example from the field of near infrared spectroscopy
More emphasis on cross-validation
Greater focus on bootstrapping
Stochastic algorithms for finding good subsets from large numbers of predictors when an exhaustive search is not feasible
Software available on the Internet for implementing many of the algorithms presented
Subset Selection in Regression, Second Edition remains dedicated to the techniques for fitting and choosing models that are linear in their parameters and to understanding and correcting the bias introduced by selecting a model that fits only slightly better than others. The presentation is clear, concise, and belongs on the shelf of anyone researching, using, or teaching subset selecting techniques.
Table of Contents
Introduction. Least-Squares Computations. Finding Subsets which Fit Well. Hypothesis Testing. How Many Variables? Estimation after Model Selection. Bayesian Methods. Conclusions.