.png){kind=icon}
返回
CONSISTENCY OF BAYESIAN PROCEDURES FOR VARIABLE SELECTION
.png){kind=icon}
OA
AI
DOI：10.1214/08-AOS606.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
It has long been known that for the comparison of pairwise nested models, a decision based on the Bayes factor produces I consistent model selector (in the frequentist sense). Here we go beyond the usual consistency for nested pairwise models, and show that for a wide class of prior distributions, including intrinsic priors, the corresponding Bayesian procedure for variable selection in normal regression is consistent in the entire class of normal linear models. We find that the asymptotics of the Bayes factors for intrinsic priors are equivalent to those of the Schwarz (BIC) criterion. Also, recall that the Jeffreys-Lindley paradox refers to the well-known fact that a point null hypothesis on the normal mean parameter is always accepted when the variance of the conjugate prior goes to infinity. This implies that some limiting forms of proper prior distributions are not necessarily Suitable for testing problems. Intrinsic priors are limits of proper prior distributions, and for finite sample sizes they have been proved to behave extremely well for variable selection in regression;,I consequence of our results is that for intrinsic priors Lindley's paradox does not arise.
Keyword:
Bayes factors
intrinsic priors
linear models
consistency
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‘It's surprising how differently they treat you’: a qualitative analysis of trainee reflections on a new programme for generalist doctors.png)
BMJ Open
OAAI
BMJ OpenIF0
OAAI分享收藏Simultaneous removal of fluorine and dissolved silica from semiconductor wastewater by an in-situ crystal nucleation method towards near-zero liquid dischargeDesalination
DesalinationIF0
PREAI
分享收藏
查看更多内容