Regression and Time Series Model Selection

Describes procedures for selecting a model from a large set of competing statistical models. The text includes: model-selection techniques for univariate and multivariate regression models; univariate and multivariate autoregressive models; nonparametric (including wavelets) and semi-parametric mode [...]
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Describes procedures for selecting a model from a large set of competing statistical models. The text includes: model-selection techniques for univariate and multivariate regression models; univariate and multivariate autoregressive models; nonparametric (including wavelets) and semi-parametric 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 range of models.