This book by Professor Chih-Ling Tsai and co-author Allan D. R. McQuarrie from North Dakota State University 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.