Multimodel inference - understanding AIC and BIC in model selection
Multimodel inference - understanding AIC and BIC in model selection
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DOI：
10.1177/0049124104268644
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发表时间：
2004-11-01
影响因子：
6.3
通讯作者：
Anderson, DR
中科院分区：
文献类型：
--
作者：
Burnham, KP;Anderson, DR
模型选择的文献一般都反映了赤池信息准则（AIC）的深厚基础，并在适当的比较贝叶斯信息准则（BIC）。AIC有一个明确的理念，一个基于信息论的健全标准，以及一个严格的统计基础。AIC可以被证明是贝叶斯使用“精明”先验模型，这是一个函数的样本大小和模型参数的数量。此外，BIC可以作为非贝叶斯结果导出。因此，关于使用AIC与BIC进行模型选择的争论不能从贝叶斯与频率论的角度来看。关于现实、近似模型和基于模型的推理的意图的假设的哲学背景应该决定使用AIC还是BIC。这里介绍了这种多模型推理的各个方面，特别是模型平均的方法。
The model selection literature has been generally poor at reflecting the deep foundations of the Akaike information criterion (AIC) and at making appropriate comparisons to the Bayesian information criterion (BIC). There is a clear philosophy a sound criterion based in information theory, and a rigorous statistical foundation for AIC. AIC can be justified as Bayesian using a "savvy" prior on models that is a function of sample Size and the number of model parameters. Furthermore, BIC can be derived as a non-Bayesian result. Therefore, arguments about using AIC versus BIC for model selection cannot be from a Bayes versus frequentist perspective. The philosophical context of what is assumed about reality, approximating models, and the intent of model-based inference should determine whether AIC or BIC is used. Various facets of such multimodel inference are presented here, particularly methods of model averaging.