ENGLISH

Information Criteria and Statistical Modeling

By Sadanori Konishi , Genshiro Kitagawa

Book information

Publisher
Springer
Year
2008
ISBN
9780387718866, 0387718869, 9780387718873, 0387718877
ISSN
0172-7397
Language
english
Format
PDF
Filesize
4 MB (4540588 bytes)
Series
Springer Series in Statistics
Pages
282\282
Library
mexmat
Time added
2009-07-20 03:45:11

Description

Winner of the 2009 Japan Statistical Association Publication Prize.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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