一种用于脆弱模型中贝叶斯推断的蒙特卡罗方法。
A Monte Carlo method for Bayesian inference in frailty models.
作者信息
Clayton D G
机构信息
Department of Community Health, University of Leicester, United Kingdom.
出版信息
Biometrics. 1991 Jun;47(2):467-85.
Many analyses in epidemiological and prognostic studies and in studies of event history data require methods that allow for unobserved covariates or "frailties." Clayton and Cuzick (1985, Journal of the Royal Statistical Society, Series A 148, 82-117) proposed a generalization of the proportional hazards model that implemented such random effects, but the proof of the asymptotic properties of the method remains elusive, and practical experience suggests that the likelihoods may be markedly nonquadratic. This paper sets out a Bayesian representation of the model in the spirit of Kalbfleisch (1978, Journal of the Royal Statistical Society, Series B 40, 214-221) and discusses inference using Monte Carlo methods.
流行病学和预后研究以及事件历史数据研究中的许多分析都需要能够处理未观测协变量或“脆弱性”的方法。克莱顿和库齐克（1985年，《皇家统计学会杂志》，A辑148卷，82 - 117页）提出了比例风险模型的一种推广形式，该模型纳入了此类随机效应，但该方法渐近性质的证明仍然难以捉摸，而且实践经验表明似然函数可能明显非二次。本文本着卡尔弗莱什（1978年，《皇家统计学会杂志》，B辑40卷，214 - 221页）的精神给出了该模型的贝叶斯表示，并讨论了使用蒙特卡罗方法进行的推断。
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