Sequential ordinal modeling with applications to survival data
Sequential ordinal modeling with applications to survival data
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DOI：
10.1111/j.0006-341x.2001.00829.x
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发表时间：
2001-09-01
期刊：
影响因子：
1.9
通讯作者：
Chib, S
中科院分区：
文献类型：
--
作者：
Albert, JH;Chib, S
本文研究了一类有序响应数据的有序模型与其他模型的关系。基于Albert和Chib （1993, Journal of American Statistical Association, 88, 669 -679）的方法，开发了用于这些模型拟合的马尔可夫链蒙特卡罗（MCMC）算法。并以心脏手术患者住院时间的实际数据为例，详细说明了这些思想和方法。该分析的一个值得注意的方面是基于边际似然和训练样本先验的几种非嵌套模型的比较，如顺序模型、累积序数模型、威布尔和逻辑逻辑模型。
This paper considers the class of sequential ordinal models in relation to other models for ordinal response data. Markov chain Monte Carlo (MCMC) algorithms, based on the approach of Albert and Chib (1993, Journal of the American Statistical Association 88, 669 -679), are developed for the fitting of these models. The ideas and methods are illustrated in detail with a real data example on the length of hospital stay for patients undergoing heart surgery. A notable aspect of this analysis is the comparison, based on marginal likelihoods and training sample priors, of several nonnested models, such as the sequential model, the cumulative ordinal model, and Weibull and log-logistic models.