FITTING AND COMPARISON OF MODELS FOR MULTIVARIATE ORDINAL OUTCOMES
FITTING AND COMPARISON OF MODELS FOR MULTIVARIATE ORDINAL OUTCOMES
复制标题
多元序数结果模型的拟合和比较
DOI：
10.1016/s0731-9053(08)23004-5
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发表时间：
2008
期刊：
影响因子：
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通讯作者：
Mark Kutzbach
中科院分区：
文献类型：
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作者：
Ivan Jeliazkov;J. Graves;Mark Kutzbach
在这篇文章中，我们考虑了在Albert和Chib(1993)的潜在变量推理框架下对单变量和多变量顺序结果的模型分析。我们回顾了几种可供选择的建模和辨识方案，并用马尔科夫链蒙特卡罗模拟方法评估了每种方案对估计的帮助或阻碍。对于每种辨识方案，我们还讨论了用边际似然和贝叶斯因子进行模型比较的问题。此外，我们开发了一个基于模拟的框架，用于分析协变量效应，该框架可以提供结果的可解释性，尽管模型中存在非线性和可以实施的不同识别限制。这些方法被用来分析劳动经济学(教育程度)、政治经济学(选民意见)和健康经济学(消费者对替代医疗信息来源的依赖)中的问题。
In this paper, we consider the analysis of models for univariate and multivariate ordinal outcomes in the context of the latent variable inferential framework of Albert and Chib (1993). We review several alternative modeling and identification schemes and evaluate how each aids or hampers estimation by Markov chain Monte Carlo simulation methods. For each identification scheme we also discuss the question of model comparison by marginal likelihoods and Bayes factors. In addition, we develop a simulation-based framework for analyzing covariate effects that can provide interpretability of the results despite the nonlinearities in the model and the different identification restrictions that can be implemented. The methods are employed to analyze problems in labor economics (educational attainment), political economy (voter opinions), and health economics (consumers’ reliance on alternative sources of medical information).