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008			150903s2008 xxu| o |||| 0|eng d
020			_a9780387718873 _99780387718873
024	7		_a10.1007/9780387718873 _2doi
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050		4	_aQA276-280
100	1		_aKonishi, Sadanori. _eautor _9304833
245	1	0	_aInformation Criteria and Statistical Modeling / _cby Sadanori Konishi, Genshiro Kitagawa.
264		1	_aNew York, NY : _bSpringer New York, _c2008.
300			_axii, 276 páginas _brecurso en línea.
336			_atexto _btxt _2rdacontent
337			_acomputadora _bc _2rdamedia
338			_arecurso en línea _bcr _2rdacarrier
347			_aarchivo de texto _bPDF _2rda
490	0		_aSpringer Series in Statistics, _x0172-7397
500			_aSpringer eBooks
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.
590			_aPara consulta fuera de la UANL se requiere clave de acceso remoto.
700	1		_aKitagawa, Genshiro. _eautor _9304834
710	2		_aSpringerLink (Servicio en línea) _9299170
776	0	8	_iEdición impresa: _z9780387718866
856	4	0	_uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/978-0-387-71887-3 _zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
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