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

页码：443 / 459

页数：17

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