A latent variable probit model for multivariate ordered categorical responses

Nikele, M. and Fahrmeir, Ludwig (1999): A latent variable probit model for multivariate ordered categorical responses. Collaborative Research Center 386, Discussion Paper 164

Abstract

This paper presents a fully Bayesian approach via Gibbs sampling for MIMIC models with ordered categorical outcomes. The method is of particular interest for moderate or medium sample size data situations as in the study to be presented. Compared to frequentist methods that are based on large sample theory, estimates and standard errors of parameters are more reliable. Experience from simulations and the application to the particular study on changes of styles of marital conflict resolution suggest that the approach provides a useful supplementary tool in combination with traditional methods.

• Item Type: Paper (Research Paper)
• Keywords: Bayesian inference, Gibbs sampling, latent variable models, Markov chain Monte Carlo, MIMIC models, ordered categorical responses, styles of marital conflict resolution
• Collections: Mathematics, Computer Science and Statistics > Statistics > Collaborative Research Center 386; Special Research Fields > Special Research Field 386
• Subjects: 500 Science > 510 Mathematics