A Bayesian semi-parametric bivariate failure time model

被引：2

作者：

Nieto-Barajas, Luis E.

Walker, Stephen G.

机构：

[1] ITAM, Dept Estadist, Mexico City 01000, DF, Mexico

[2] Univ Kent, Inst Math Stat & Actuarial Sci, Canterbury CT2 7N2, Kent, England

来源：

COMPUTATIONAL STATISTICS & DATA ANALYSIS | 2007年 / 51卷 / 12期

基金：

英国工程与自然科学研究理事会;

关键词：

Bayes nonparametrics; bivariate survival analysis; copula; correlated frailty model; discrete Markov gamma process; latent variables; mixture representation;

D O I：

10.1016/j.csda.2006.12.020

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

In this paper we introduce a Bayesian semiparametric model for bivariate and multivariate survival data. The marginal densities are well-known nonparametric survival models and the joint density is constructed via a mixture. Our construction also defines a copula and the properties of this new copula are studied. We also consider the model in the presence of covariates and, in particular, we find a simple generalisation of the widely used frailty model, which is based on a new bivariate gamma distribution. (C) 2007 Elsevier B.V. All rights reserved.

引用

页码：6102 / 6113

页数：12

共 34 条

[1] A multivariate Gompertz-type distribution [J].