artículo
Nonparametric Bayesian modeling for multivariate ordinal data
Fecha
2005Registro en:
10.1198/106186005X63185
1537-2715
1061-8600
WOS:000235042000006
Autor
Kottas, A
Muller, P
Quintana, F
Institución
Resumen
This article proposes a probability model for kappa-dimensional ordinal outcomes, that is, it considers inference for data recorded in kappa-dimensional contingency tables with ordinal factors. The proposed approach is based on full posterior inference, assuming a flexible underlying prior probability model for the contingency table cell probabilities. We use a variation of the traditional multivariate probit model, with latent scores that determine the observed data. In our model, a mixture of normals prior replaces the usual single multivariate normal model for the latent variables. By augmenting the prior model to a mixture of normals we generalize inference in two important ways. First, we allow for varying local dependence structure across the contingency table. Second, inference in ordinal multivariate probit models is plagued by problems related to the choice and resampling of cutoffs defined for these latent variables. We show how the proposed mixture model approach entirely removes these problems. We illustrate the methodology with two examples, one simulated dataset and one dataset of interrater agreement.