Information Criteria and Statistical Modeling

His primary interests are in time series analysis, non-Gaussian nonlinear filtering and statistical modeling. He is the executive editor of the Annals of theInstitute of Statistical Mathematics, co-author of Smoothness Priors Analysis of Time Series, Akaike Information Criterion Statistics, and seve...

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Bibliographic Details
Main Authors: Konishi, Sadanori, Kitagawa, Genshiro (Author)
Format: eBook
Language:English
Published: New York, NY Springer New York 2008, 2008
Edition:1st ed. 2008
Series:Springer Series in Statistics
Subjects:
Online Access:
Collection: Springer eBooks 2005- - Collection details see MPG.ReNa
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505 0 |a Concept of Statistical Modeling -- Statistical Models -- Information Criterion -- Statistical Modeling by AIC -- Generalized Information Criterion (GIC) -- Statistical Modeling by GIC -- Theoretical Development and Asymptotic Properties of the GIC -- Bootstrap Information Criterion -- Bayesian Information Criteria -- Various Model Evaluation Criteria 
653 |a Mathematical statistics 
653 |a Coding and Information Theory 
653 |a Statistical Theory and Methods 
653 |a Coding theory 
653 |a Computer science / Mathematics 
653 |a Probability and Statistics in Computer Science 
653 |a Computer simulation 
653 |a Statistics  
653 |a Computer Modelling 
653 |a Data mining 
653 |a Information theory 
653 |a Mathematical Modeling and Industrial Mathematics 
653 |a Data Mining and Knowledge Discovery 
653 |a Mathematical models 
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520 |a His primary interests are in time series analysis, non-Gaussian nonlinear filtering and statistical modeling. He is the executive editor of the Annals of theInstitute of Statistical Mathematics, co-author of Smoothness Priors Analysis of Time Series, Akaike Information Criterion Statistics, and several Japanese books. He was awarded the Japan Statistical Society Prize in 1997 and Ishikawa Prize in 1999, and is a Fellow of the American Statistical Association 
520 |a 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.  
520 |a 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. Sadanori Konishi is Professor of Faculty of Mathematics at Kyushu University. His primary research interests are in multivariate analysis, statistical learning, pattern recognition and nonlinear statistical modeling. He is the editor of the Bulletin of Informatics and Cybernetics and is co-author of several Japanese books. He was awarded the Japan Statistical Society Prize in 2004 and is a Fellow of the American Statistical Association. Genshiro Kitagawa is Director-General of the Institute of Statistical Mathematics and Professor of Statistical Science at the Graduate University for Advanced Study.