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. Contents:The Univariate Regression ModelThe Univariate Autoregressive ModelThe Multivariate Regression ModelThe Vector Autoregressive ModelCross-Validation and the BootstrapRobust Regression and Quasi-LikelihoodNonparametric Regression and WaveletsSimulations and Examples Readership: Statisticians, biostatisticians, applied mathematicians, engineers and economists. keywords:Information Theory;Model Selection;Regression;Time Series;Nonparametric;Multivariate;Cross Validation;Bootstrap “… is a good reference on model selection and a valuable addition to any statistical library. It can be used as a textbook in a graduate level course and will be very useful for someone starting research in this field.” Journal of the American Statistical Association “The presented materials can serve as a reference book for specialists and also as an important resource of information for statisticians dealing with applications.” Mathematics Abstracts