Empirical Bayes vs. fully Bayes variable selection
Empirical Bayes vs. fully Bayes variable selection
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DOI：
10.1016/j.jspi.2007.02.011
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发表时间：
2008-04-01
影响因子：
0.9
通讯作者：
George, Edward I.
中科院分区：
文献类型：
--
作者：
Cui, Wen;George, Edward I.
对于正态线性模型的变量选择问题，AIC、C-p、BIC和RIC等定额罚款选择准则对应于不同固定超参数设置下的分层贝叶斯模型的后验模型。George和Foster[2000]已经证明了通过超参数的经验贝叶斯估计获得的自适应选择标准。校准和经验贝叶斯变量选择。比普里斯卡87(4)，731-747]，以改进这些固定的选择标准。在这篇文章中，我们研究了替代的完全贝叶斯方法的潜力，而不是相对于先验分布的超参数。考虑了几种结构化的先验公式，得到了它们的完全Bayes选择和估计方法。研究了与经验贝叶斯方法的解析和模拟比较。(C)2007 Elsevier B.V.保留所有权利。
For the problem of variable selection for the normal linear model, fixed penalty selection criteria such as AIC, C-p, BIC and RIC correspond to the posterior modes of a hierarchical Bayes model for various fixed hyperparameter settings. Adaptive selection criteria obtained by empirical Bayes estimation of the hyperparameters have been shown by George and Foster [2000. Calibration and Empirical Bayes variable selection. Biometrika 87(4), 731-747] to improve on these fixed selection criteria. In this paper, we study the potential of alternative fully Bayes methods, which instead margin out the hyperparameters with respect to prior distributions. Several structured prior formulations are considered for which fully Bayes selection and estimation methods are obtained. Analytical and simulation comparisons with empirical Bayes counterparts are studied. (C) 2007 Elsevier B.V. All rights reserved.