Mixture model of Gaussian copulas to cluster mixed-type data
نویسنده
چکیده
A mixture model of Gaussian copulas is proposed to cluster mixed data. This approach allows to straightforwardly define simple multivariate intra-class dependency models while preserving classical distributions for the one-dimensional margins of each component in order to facilitate the model interpretation. Moreover, the intra-class dependencies are taken into account by the Gaussian copulas which provide one robust correlation coefficient per couple of variables and per class. This model generalizes different existing models defined for homogeneous or mixed variables. The Bayesian inference is performed via a Metropolis-within-Gibbs sampler. The model is illustrated by a real data set clustering.
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