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Regression and Time Series Model Selection (Hardcover)by Allan D. R. McQuarrie
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(Author), Chih-Ling Tsai (Author) "A question perhaps as old as modeling is "Which variables are important?..." (more)
Key Phrases: strong penalty functions, overfit excessively, overfitting properties, Monte Carlo, Central Case, Selected Derivations of Model Selection Criteria (more...)
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...serve as a reference for specialists and also as an important resource of information for statisticians dealing with applications. -- Mathematics Abstracts
Product Description
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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