000			04471nam a22005415i 4500
001			978-0-387-71887-3
003			DE-He213
005			20250710084014.0
007			cr nn 008mamaa
008			100301s2008 xxu| s |||| 0|eng d
020			_a9780387718873 _a99780387718873
024	7		_a10.1007/978-0-387-71887-3 _2doi
082	0	4	_a519.5 _223
100	1		_aKonishi, Sadanori. _eauthor.
245	1	0	_aInformation Criteria and Statistical Modeling _h[recurso electrónico] / _cby Sadanori Konishi, Genshiro Kitagawa.
264		1	_aNew York, NY : _bSpringer New York, _c2008.
300			_aXII, 276 p. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_arecurso en línea _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aSpringer Series in Statistics, _x0172-7397
505	0		_aConcept 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.
520			_aWinner 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. 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. His primary interests are in time series analysis, non-Gaussian nonlinear filtering and statistical modeling. He is the executive editor of the Annals of the Institute 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.
650		0	_aSTATISTICS.
650		0	_aCOMPUTER SCIENCE.
650		0	_aDATA MINING.
650		0	_aCOMPUTER SIMULATION.
650		0	_aBIOINFORMATICS.
650		0	_aMATHEMATICAL STATISTICS.
650		0	_aECONOMETRICS.
650	1	4	_aSTATISTICS.
650	2	4	_aSTATISTICAL THEORY AND METHODS.
650	2	4	_aDATA MINING AND KNOWLEDGE DISCOVERY.
650	2	4	_aPROBABILITY AND STATISTICS IN COMPUTER SCIENCE.
650	2	4	_aSIMULATION AND MODELING.
650	2	4	_aCOMPUTATIONAL BIOLOGY/BIOINFORMATICS.
650	2	4	_aECONOMETRICS.
700	1		_aKitagawa, Genshiro. _eauthor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9780387718866
830		0	_aSpringer Series in Statistics, _x0172-7397
856	4	0	_uhttp://dx.doi.org/10.1007/978-0-387-71887-3 _zVer el texto completo en las instalaciones del CICY
912			_aZDB-2-SMA
942			_2ddc _cER
999			_c58306 _d58306