| Information Criteria and Statistical Modeling 2008 Edition Contributor(s): Konishi, Sadanori (Author), Kitagawa, Genshiro (Author) |
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ISBN: 0387718869 ISBN-13: 9780387718866 Publisher: Springer OUR PRICE: $151.99 Product Type: Hardcover - Other Formats Published: October 2007 Annotation: The Akaike information criterion (AIC) derived as an estimator of the Kullback-Leibler information discrepancy provides a useful tool for evaluating statistical models, and numerous successful applications of the AIC have been reported in various fields of natural sciences, social sciences and engineering. One of the main objectives of this book is to provide comprehensive explanations of the concepts and derivations of the AIC and related criteria, including Schwarz's Bayesian information criterion (BIC), together with a wide range of practical examples of model selection and evaluation criteria. A secondary objective is to provide a theoretical basis for the analysis and extension of information criteria via a statistical functional approach. A generalized information criterion (GIC) and a bootstrap information criterion are presented, which provide unified tools for modeling and model evaluation for a diverse range of models, including various types of nonlinear models and model estimation procedures such as robust estimation, the maximum penalized likelihood method and a Bayesian approach. |
| Additional Information |
| BISAC Categories: - Mathematics | Probability & Statistics - General - Computers | Computer Science - Computers | Databases - Data Mining |
| Dewey: 519.22 |
| LCCN: 2007925718 |
| Series: Springer Series in Statistics |
| Physical Information: 0.7" H x 6.3" W x 9.3" (1.15 lbs) 292 pages |
| Descriptions, Reviews, Etc. |
| Publisher Description: The Akaike information criterion (AIC) derived as an estimator of the Kullback-Leibler information discrepancy provides a useful tool for evaluating statistical models, and numerous successful applications of the AIC have been reported in various fields of natural sciences, social sciences and engineering. One of the main objectives of this book is to provide comprehensive explanations of the concepts and derivations of the AIC and related criteria, including Schwarz's Bayesian information criterion (BIC), together with a wide range of practical examples of model selection and evaluation criteria. A secondary objective is to provide a theoretical basis for the analysis and extension of information criteria via a statistical functional approach. A generalized information criterion (GIC) and a bootstrap information criterion are presented, which provide unified tools for modeling and model evaluation for a diverse range of models, including various types of nonlinear models and model estimation procedures such as robust estimation, the maximum penalized likelihood method and a Bayesian approach. |