.png){kind=icon}
返回
BAYESIAN VARIABLE SELECTION WITH SHRINKING AND DIFFUSING PRIORS
.png){kind=icon}
OA
AI
DOI：10.1214/14-AOS1207.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
We consider a Bayesian approach to variable selection in the presence of high dimensional covariates based on a hierarchical model that places prior distributions on the regression coefficients as well as on the model space. We adopt the well-known spike and slab Gaussian priors with a distinct feature, that is, the prior variances depend on the sample size through which appropriate shrinkage can be achieved. We show the strong selection consistency of the proposed method in the sense that the posterior probability of the true model converges to one even when the number of covariates grows nearly exponentially with the sample size. This is arguably the strongest selection consistency result that has been available in the Bayesian variable selection literature; yet the proposed method can be carried out through posterior sampling with a simple Gibbs sampler. Furthermore, we argue that the proposed method is asymptotically similar to model selection with the Lo penalty. We also demonstrate through empirical work the fine performance of the proposed approach relative to some state of the art alternatives.
Keyword:
Bayes factor
hierarchical model
high dimensional data
shrinkage
variable selection
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.7
论文数:
2.8K
被引数:
2.9W
机构
引用论文
分享收藏
分享收藏Bayesian variable selection for high dimensional generalized linear models: Convergence rates of the fitted densities.png)
ANNALS OF STATISTICSOAAI
ANNALS OF STATISTICSIF3.7
OAAI分享收藏Nonconcave penalized likelihood with a diverging number of parameters具有不同数量参数的非凹惩罚似然ANNALS OF STATISTICSOAAI
ANNALS OF STATISTICSIF3.7
OAAI分享收藏
查看更多内容