Bayesian variable and link determination for generalised linear models

被引：57
|
作者：
Ntzoufras, I
Dellaportas, P
Forster, JJ
机构：
[1] Athens Univ Econ & Business, Dept Stat, Athens 10434, Greece
[2] Univ Southampton, Dept Math, Southampton SO9 5NH, Hants, England
来源：
JOURNAL OF STATISTICAL PLANNING AND INFERENCE | 2003年 / 111卷 / 1-2期
关键词：
logistic regression; Markov chain Monte-Carlo; reversible jump;
D O I：
10.1016/S0378-3758(02)00298-7
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：
In this paper, we describe full Bayesian inference for generalised linear models where uncertainty exists about the structure of the linear predictor, the linear parameters and the link function. Choice of suitable prior distributions is discussed in detail and we propose an efficient reversible jump Markov chain Monte-Carlo algorithm for calculating posterior summaries. We illustrate our method with two data examples. (C) 2002 Elsevier Science B.V. All rights reserved.
引用
收藏
页码：165 / 180
页数：16
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