| 書目名稱 | Information Criteria and Statistical Modeling |
| 編輯 | Sadanori Konishi,Genshiro Kitagawa |
| 視頻video | http://file.papertrans.cn/466/465019/465019.mp4 |
| 概述 | With the development of modeling techniques, it has been required to construct model selection criteria, relaxing the assumptions imposed AIC and BIC.Includes supplementary material: |
| 叢書名稱 | Springer Series in Statistics |
| 圖書封面 |  |
| 描述 | .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. . |
| 出版日期 | Book 2008 |
| 關鍵詞 | Akaike information criterion; Bayesian approach; Computer; Estimator; Information; Likelihood; Statistical |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-0-387-71887-3 |
| isbn_softcover | 978-1-4419-2456-8 |
| isbn_ebook | 978-0-387-71887-3Series ISSN 0172-7397 Series E-ISSN 2197-568X |
| issn_series | 0172-7397 |
| copyright | Springer-Verlag New York 2008 |
|Archiver|手機版|小黑屋|
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