Editorial: Advances in Mixture Models
Computational Statistics & Data Analysis
Generalized linear mixed model with a penalized Gaussian mixture as a random effects distribution
Computational Statistics & Data Analysis
Bayesian analysis of two dependent 2×2 contingency tables
Computational Statistics & Data Analysis
A Bayesian approach to model-based clustering for binary panel probit models
Computational Statistics & Data Analysis
Similarity analysis in Bayesian random partition models
Computational Statistics & Data Analysis
Probabilistic self-organizing maps for qualitative data
Neural Networks
Classification of the action surface EMG signals based on the dirichlet process mixtures method
ICIRA'11 Proceedings of the 4th international conference on Intelligent Robotics and Applications - Volume Part I
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The multivariate probit model is a popular choice for modelling correlated binary responses. It assumes an underlying multivariate normal distribution dichotomized to yield a binary response vector. Other choices for the latent distribution have been suggested, but basically all models assume homogeneity in the correlation structure across the subjects. When interest lies in the association structure, relaxing this homogeneity assumption could be useful. The latent multivariate normal model is replaced by a location and association mixture model defined by a Dirichlet process. Attention is paid to the parameterization of the covariance matrix in order to make the Bayesian computations convenient. The approach is illustrated on a simulated data set and applied to oral health data from the Signal Tandmobiel^(R) study to examine the hypothesis that caries is mainly a spatially local disease.