Generalised information criteria in model selection
Generalised information criteria in model selection
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DOI：
10.1093/biomet/83.4.875
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发表时间：
1996-12-01
期刊：
影响因子：
2.7
通讯作者：
Kitagawa, G
中科院分区：
文献类型：
--
作者：
Konishi, S;Kitagawa, G
从信息论的角度研究了统计模型优度的评价问题。信息准则提出了评估模型的各种估计程序时，指定的家庭的概率分布不包含的分布生成的数据。所提出的标准适用于评估模型估计的最大似然，强大的，惩罚似然，贝叶斯程序等，我们还讨论了使用的引导模型评估问题，并提出了方差减少技术的引导模拟。
The problem of evaluating the goodness of statistical models is investigated from an information-theoretic point of view. Information criteria are proposed for evaluating models constructed by various estimation procedures when the specified family of probability distributions does not contain the distribution generating the data. The proposed criteria are applied to the evaluation of models estimated by maximum likelihood, robust, penalised likelihood, Bayes procedures, etc. We also discuss the-use of the bootstrap in model evaluation problems and present a variance reduction technique in the bootstrap simulation.