Regression and Time Series Model Selection

This important book describes procedures for selecting a model from a large set of competing statistical models. It includes model selection techniques for univariate and multivariate regression models, univariate and multivariate autoregressive models, nonparametric (including wavelets) and semiparametric regression models, and quasi-likelihood and robust regression models. Information-based model selection criteria are discussed, and small sample and asymptotic properties are presented. The book also provides examples and large scale simulation studies comparing the performances of information-based model selection criteria, bootstrapping, and cross-validation selection methods over a wide range of models.

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Connects many different aspects of the growing model selection field by examining different lines of reasoning that have motivated derivation of both classical and modern criteria, and then examining the performance of these criteria to see how well it matches with the intent of their creators. Useful as a guide to researchers, and as a resource for practicing statisticians for matching appropriate selection criteria to a given problem or data set. Contains chapters on univariate regression and autoregressive models, cross-validation and the bootstrap, robust regression and quasi-liklihood, and nonparametric regression and wavelets. The authors are affiliated with North Dakota State University, and the University of California-Davis. Annotation c. Book News, Inc., Portland, OR (booknews.com)