by Allan D R McQuarrie (North Dakota State University) & Chih-Ling Tsai (University of California, Davis)

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.

• The Univariate Regression Model
• The Univariate Autoregressive Model
• The Multivariate Regression Model
• The Vector Autoregressive Model
• Cross-Validation and the Bootstrap
• Robust Regression and Quasi-Likelihood
• Nonparametric Regression and Wavelets
• Simulations and Examples