Information Criteria and Statistical Modeling (inbunden)
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Format
Inbunden (Hardback)
Språk
Engelska
Serie
Springer Series in Statistics
Antal sidor
276
Utgivningsdatum
2007-10-12
Förlag
Springer-Verlag New York Inc.
Dimensioner
236 x 160 x 18 mm
Vikt
522 g
ISBN
9780387718866

Information Criteria and Statistical Modeling

av Sadanori Konishi, Genshiro Kitagawa
Inbunden,  Engelska, 2007-10-12
1538
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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.

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