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Bayesian Analysis of Multivariate Probit Models with Surrogate Outcome Data

Published online by Cambridge University Press:  01 January 2025

Wai-Yin Poon and
Hai-Bin Wang
Wai-Yin Poon*
Affiliation:
The Chinese University of Hong Kong
Hai-Bin Wang
Affiliation:
Xiamen University
*
Requests for reprints should be sent to Wai-Yin Poon, Department of Statistics, The Chinese University of Hong Kong, Shatin, Hong Kong, China. E-mail: wypoon@cuhk.edu.hk
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Abstract

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.

Keywords

errors-in-variablesGibbs samplerMetropolis–Hastings algorithmmisclassificationmultivariate probit modelparameter expansionsurrogate variable

Information

Type
Original Paper
Information
Psychometrika , Volume 75 , Issue 3 , September 2010, pp. 498 - 520
Copyright
Copyright © 2010 The Psychometric Society

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