A Multivariate Mixture Regression Model for Constrained Responses

Screening for a Multivariate Mixture Normal Distribution

Constrained monotone EM algorithms for finite mixture of multivariate Gaussians

The likelihood function for normal multivariate mixtures may present both local spurious maxima and also singularities and the latter may cause the failure of the optimization algorithms. Theoretical results assure that imposing some constraints on the eigenvalues of the covariance matrices of the multivariate normal components leads to a constrained parameter space with no singularities and at...

screening for a multivariate mixture normal distribution

A Mixture Probabilistic PCA Model for Multivariate Manufacturing Processes Monitoring

− A mixture probabilistic Principal Component Analysis (PCA) model is proposed as a multivariate process monitoring tool in this paper. High dimensional measurement data could be aggregated into some clusters based on the mixture distribution model, where the number of these clusters is automatically determined by the maximum likelihood estimation procedure. The multivariate statistical process...

Bayesian Dirichlet mixture model for multivariate extremes: A re-parametrization

The probabilistic framework of extreme value theory is well-known: the dependence structure of large events is characterized by an angular measure on the positive orthant of the unit sphere. The family of these angular measures is non-parametric by nature. Nonetheless, any angular measure may be approached arbitrarily well by a mixture of Dirichlet distributions. The semi-parametric Dirichlet m...