Composite Likelihood Bayesian Information Criteria for Model Selection in High-Dimensional Data
Composite Likelihood Bayesian Information Criteria for Model Selection in High-Dimensional Data
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DOI：
10.1198/jasa.2010.tm09414
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发表时间：
2010-12-01
影响因子：
3.7
通讯作者：
Song, Peter X. -K.
中科院分区：
文献类型：
--
作者：
Gao, Xin;Song, Peter X. -K.
对于具有复杂依赖结构的高维数据集，全似然方法往往导致难以处理的计算复杂度。考虑到大多数传统使用的信息标准需要评估完全可能性，这给模型选择带来了困难。我们提出了一个复合似然版本的贝叶斯信息准则（BIC），并建立其一致性的选择真正的底层边际模型。我们提出的BIC被证明是选择一致的一些温和的正则性条件下，允许潜在的模型参数的数量增加到无穷大，在一定的速度的样本量。仿真研究表明，这种新的BIC的经验性能，特别是对于的情况下，参数的数量随着样本大小的增加。在线补充材料中提供了我们理论结果的技术证明。
For high-dimensional data sets with complicated dependency structures, the full likelihood approach often leads to intractable computational complexity. This imposes difficulty on model selection, given that most traditionally used information criteria require evaluation of the full likelihood. We propose a composite likelihood version of the Bayes information criterion (BIC) and establish its consistency property for the selection of the true underlying marginal model. Our proposed BIC is shown to be selection-consistent under some mild regularity conditions, where the number of potential model parameters is allowed to increase to infinity at a certain rate of the sample size. Simulation studies demonstrate the empirical performance of this new BIC, especially for the scenario where the number of parameters increases with sample size. Technical proofs of our theoretical results are provided in the online supplemental materials.