VARIABLE SELECTION VIA GIBBS SAMPLING
VARIABLE SELECTION VIA GIBBS SAMPLING
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DOI：
10.2307/2290777
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发表时间：
1993-09-01
影响因子：
3.7
通讯作者：
MCCULLOCH, RE
中科院分区：
文献类型：
--
作者：
GEORGE, EI;MCCULLOCH, RE
构建多元回归模型的一个关键问题是选择要包含的预测变量。本文的主旨是提出并开发一种使用概率考虑因素来选择有希望的子集的程序。此过程需要将回归设置嵌入分层正态混合模型中，其中使用潜在变量来识别子集选择。在这个框架中，有希望的预测子集可以被识别为具有较高后验概率的子集。然后，通过使用吉布斯采样器从可能的子集选择集的多项后验分布中间接采样，可以减轻计算负担。那些概率较高的子集（有希望的子集）可以通过它们在吉布斯样本中更频繁的出现来识别。
A crucial problem in building a multiple regression model is the selection of predictors to include. The main thrust of this article is to propose and develop a procedure that uses probabilistic considerations for selecting promising subsets. This procedure entails embedding the regression setup in a hierarchical normal mixture model where latent variables are used to identify subset choices. In this framework the promising subsets of predictors can be identified as those with higher posterior probability. The computational burden is then alleviated by using the Gibbs sampler to indirectly sample from this multinomial posterior distribution on the set of possible subset choices. Those subsets with higher probability-the promising ones-can then be identified by their more frequent appearance in the Gibbs sample.