Extended Bayesian information criteria for model selection with large model spaces
Extended Bayesian information criteria for model selection with large model spaces
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DOI：
10.1093/biomet/asn034
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发表时间：
2008-09-01
期刊：
影响因子：
2.7
通讯作者：
Chen, Zehua
中科院分区：
文献类型：
--
作者：
Chen, Jiahua;Chen, Zehua
当模型空间较大时，普通的贝叶斯信息准则对模型选择过于宽松。在本文中，我们重新审视贝叶斯范式的模型选择，并提出了一个扩展的家庭贝叶斯信息准则，它考虑到未知参数的数量和模型空间的复杂性。建立了它们的一致性，特别是允许协变量的数量随着样本量增加到无穷大。他们在各种情况下的表现进行了评估模拟研究。结果表明，扩展贝叶斯信息准则的积极选择率，但严格控制错误的发现率，在许多应用程序中的一个理想的属性，会产生一个小的损失。扩展贝叶斯信息准则对于样本量适中但协变量数量巨大的问题中的变量选择非常有用，特别是在全基因组关联研究中，这是遗传学研究中的一个活跃领域。
The ordinary Bayesian information criterion is too liberal for model selection when the model space is large. In this paper, we re-examine the Bayesian paradigm for model selection and propose an extended family of Bayesian information criteria, which take into account both the number of unknown parameters and the complexity of the model space. Their consistency is established, in particular allowing the number of covariates to increase to infinity with the sample size. Their performance in various situations is evaluated by simulation studies. It is demonstrated that the extended Bayesian information criteria incur a small loss in the positive selection rate but tightly control the false discovery rate, a desirable property in many applications. The extended Bayesian information criteria are extremely useful for variable selection in problems with a moderate sample size but with a huge number of covariates, especially in genome-wide association studies, which are now an active area in genetics research.