Subset selection in regression. 2nd ed.

*(English)*Zbl 1051.62060
Monographs on Statistics and Applied Probability 95. Boca Raton, FL: Chapman and Hall/CRC (ISBN 978-1-58488-171-1/hbk; 978-0-367-39622-0/pbk). xvii, 238 p. (2002).

Publisher’s description: Originally published in 1990, see the review Zbl 0702.62057, the first edition 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, this 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 this 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, more examples.

This 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.

New in this 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, more examples.

This 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.

##### MSC:

62J05 | Linear regression; mixed models |

62-02 | Research exposition (monographs, survey articles) pertaining to statistics |

65C99 | Probabilistic methods, stochastic differential equations |

62J07 | Ridge regression; shrinkage estimators (Lasso) |