BAYESIAN-INFERENCE FOR GENERALIZED LINEAR AND PROPORTIONAL HAZARDS MODELS VIA GIBBS SAMPLING

被引：149
|
作者：
DELLAPORTAS, P
SMITH, AFM
机构：
[1] UNIV LONDON IMPERIAL COLL SCI TECHNOL & MED,DEPT MATH,HUXLEY BLDG,180 QUEENS GATE,LONDON SW7 2BZ,ENGLAND
[2] UNIV NOTTINGHAM,NOTTINGHAM NG7 2RD,ENGLAND
来源：
APPLIED STATISTICS-JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES C | 1993年 / 42卷 / 03期
关键词：
ADAPTIVE REJECTION ALGORITHM; BAYESIAN INFERENCE; GENERALIZED LINEAR MODELS; GIBBS SAMPLING; PROPORTIONAL HAZARDS MODELS; QUADRATIC LOGISTIC REGRESSION;
D O I：
10.2307/2986324
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：
It is shown that Gibbs sampling, making systematic use of an adaptive rejection algorithm proposed by Gilks and Wild, provides a straightforward computational procedure for Bayesian inferences in a wide class of generalized linear and proportional hazards models.
引用
收藏
页码：443 / 459
页数：17
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