Téléchargez le livre : Information Criteria and Statistical Modeling

Information Criteria and Statistical Modeling



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Éditeur :

Springer


Collection :

Springer Series in Statistics

Paru le : 2007-09-12



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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.
Pages
276 pages
Collection
Springer Series in Statistics
Parution
2007-09-12
Marque
Springer
EAN papier
9780387718866
EAN PDF
9780387718873
Informations sur l'ebook
Nombre pages copiables
2
Nombre pages imprimables
27
Taille du fichier
5263 Ko
Ce livre est accessible aux handicaps Voir les informations sur l'accessibilité
Prix
94,94 €

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