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ERIC Number: EJ895494
Record Type: Journal
Publication Date: 2010-Sep
Pages: 23
Abstractor: As Provided
Reference Count: 40
ISSN: ISSN-0033-3123
Bayesian Analysis of Multivariate Probit Models with Surrogate Outcome Data
Poon, Wai-Yin; Wang, Hai-Bin
Psychometrika, v75 n3 p498-520 Sep 2010
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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Publication Type: Journal Articles; Reports - Descriptive
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A