Information Criteria and Statistical Modeling (Springer Series in Statistics)

@book{citeulike:2719481, abstract = {{<P>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.</P> <P>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. </P>}}, author = {Konishi, Sadanori and Kitagawa, Genshiro }, citeulike-article-id = {2719481}, howpublished = {Hardcover}, isbn = {0387718869}, keywords = {feature\_selection, information\_theory, machine\_learning, mdl, model\_selection, simplicity}, month = {October}, posted-at = {2008-04-25 22:10:06}, priority = {2}, publisher = {Springer}, title = {Information Criteria and Statistical Modeling (Springer Series in Statistics)}, url = {http://www.amazon.ca/exec/obidos/redirect?tag=citeulike09-20\&amp;path=ASIN/0387718869}, year = {2007} }

TY - BOOK ID - citeulike:2719481 L3 - citeulike-article-id:2719481 TI - Information Criteria and Statistical Modeling (Springer Series in Statistics) PB - Springer SN - 0387718869 N2 - {<P>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.</P> <P>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. </P>} KW - feature_selection KW - information_theory KW - machine_learning KW - mdl KW - model_selection KW - simplicity AU - Konishi, Sadanori AU - Kitagawa, Genshiro PY - 2007/10/12/ UR - http://www.amazon.ca/exec/obidos/redirect?tag=citeulike09-20&amp;path=ASIN/0387718869 ER -