Adaptive Monte Carlo for Bayesian Variable Selection in Regression Models

被引：18

作者：

Lamnisos, Demetris ^{[1
]}

Griffin, Jim E. ^{[2
]}

Steel, Mark F. J. ^{[3
]}

机构：

[1] Cyprus Univ Technol, Sch Hlth Sci, CY-3036 Limassol, Cyprus

[2] Univ Kent, Sch Math Stat & Actuarial Sci, Canterbury CT2 7NF, Kent, England

[3] Univ Warwick, Dept Stat, Coventry CV4 7AL, W Midlands, England

来源：

JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS | 2013年 / 22卷 / 03期

关键词：

Linear regression; Metropolis-within-Gibbs; Probit regression; GENE SELECTION; ALGORITHMS; CLASSIFICATION; METROPOLIS; DISTRIBUTIONS; BINARY;

D O I：

10.1080/10618600.2012.694756

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：

This article describes methods for efficient posterior simulation for Bayesian variable selection in generalized linear models with many regressors but few observations. The algorithms use a proposal on model space that contains a tuneable parameter. An adaptive approach to choosing this tuning parameter is described that allows automatic, efficient computation in these models. The method is applied to examples from normal linear and probit regression. Relevant code and datasets are posted online as supplementary materials.

引用

页码：729 / 748

页数：20

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