Bayesian Analysis of Multivariate Probit Models with Surrogate Outcome Data

被引：0
作者：
Wai-Yin Poon
Hai-Bin Wang
机构：
[1] The Chinese University of Hong Kong,Department of Statistics
[2] Xiamen University,School of Mathematical Sciences
来源：
Psychometrika | 2010年 / 75卷
关键词：
errors-in-variables; Gibbs sampler; Metropolis–Hastings algorithm; misclassification; multivariate probit model; parameter expansion; surrogate variable;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
A new class of parametric models that generalize the multivariate probit model and the errors-in-variables model is developed to model and analyze ordinal data. A general model structure is assumed to accommodate the information that is obtained via surrogate variables. A hybrid Gibbs sampler is developed to estimate the model parameters. To obtain a rapidly converged algorithm, the parameter expansion technique is applied to the correlation structure of the multivariate probit models. The proposed model and method of analysis are demonstrated with real data examples and simulation studies.
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页码：498 / 520
页数：22
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