A New Algorithm in Bayesian Model Averaging in Regression Models

被引：1
|
作者：
Fan, Tsai-Hung [1 ]
Wang, Guo-Tzau [2 ]
Yu, Jenn-Hwa [3 ]
机构：
[1] Natl Cent Univ, Inst Stat, Jhongli, Taiwan
[2] Natl Ctr High Performance Comp, Hsinchu, Taiwan
[3] Natl Cent Univ, Dept Math, Jhongli, Taiwan
来源：
COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION | 2014年 / 43卷 / 02期
关键词：
Ayesian model averaging; Occam's window; Regression models; GRAPHICAL MODELS; UNCERTAINTY;
D O I：
10.1080/03610918.2012.700750
中图分类号：
O21 [概率论与数理统计]; C8 [统计学];
学科分类号：
020208 ; 070103 ; 0714 ;
摘要：
We propose a new iterative algorithm, namely the model walking algorithm, to modify the widely used Occam's window method in Bayesian model averaging procedure. It is verified, by simulation, that in the regression models, the proposed algorithm is much more efficient in terms of computing time and the selected candidate models. Moreover, it is not sensitive to the initial models.
引用
收藏
页码：315 / 328
页数：14
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