Bayesian nonparametric multivariate ordinal regression
Bayesian nonparametric multivariate ordinal regression
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DOI：
10.1002/cjs.11253
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发表时间：
2015-09-01
影响因子：
0.6
通讯作者：
Hanson, Timothy E.
中科院分区：
文献类型：
--
作者：
Bao, Junshu;Hanson, Timothy E.
多元有序数据建模为一个有限的棒打破多元概率单位模型的混合物。参数多变量概率模型首先开发有序数据，然后推广到有限混合的多变量概率模型。在模拟和数据分析中，发现对先前设置的具体建议效果良好。通过检查混合物组分的各个方面以及通过集中于平均响应的平均效应来进行模型的解释。仿真验证了拟合技术的工作原理，和饮酒行为数据的分析说明了所提出的模型的实用性。(C)2015年加拿大统计学会
Multivariate ordinal data are modelled as a finite stick-breaking mixture of multivariate probit models. Parametric multivariate probit models are first developed for ordinal data, then generalized to finite mixtures of multivariate probit models. Specific recommendations for prior settings are found to work well in simulations and data analyses. Interpretation of the model is carried out by examining aspects of the mixture components as well as through averaged effects focusing on the mean responses. A simulation verifies that the fitting technique works, and an analysis of alcohol drinking behaviour data illustrates the usefulness of the proposed model. (C) 2015 Statistical Society of Canada