期刊
ANNALS OF THE INSTITUTE OF STATISTICAL MATHEMATICS
卷 67, 期 5, 页码 963-997出版社
SPRINGER HEIDELBERG
DOI: 10.1007/s10463-014-0483-8
关键词
Model selection consistency; Loss efficiency; Variable selection; g-prior
Consider Bayesian variable selection in normal linear regression models based on Zellner's -prior. We study theoretical properties of this method when the sample size grows and consider the cases when the number of regressors, is fixed and when it grows with . We first consider the situation where the true model is not in the model space and prove under mild conditions that the method is consistent and loss efficient in appropriate sense. We then consider the case when the true model is in the model space and prove that the posterior probability of the true model goes to one as goes to infinity. Loss efficiency is also proved in this situation. We give explicit conditions on the rate of growth of , possibly depending on that of as grows, for our results to hold. This helps in making recommendations for the choice of g.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据