In Bayesian hierarchical modeling, it is often appealing to allow the conditional density of an (observable or unobservable) random variable Y to change flexibly with categorical and continuous predictors X. A mixture of regression models is proposed, with the mixture distribution varying with X. Treating the smoothing parameters and number of mixture components as unknown, the MLE does not exi...

Portfolios of assets whose returns have the Gaussian mixture distribution are optimized in the static setting to find portfolio weights and efficient frontiers using the probability of outperforming a target return and Hodges’ modified Sharpe ratio objective functions. The sensitivities of optimal portfolio weights to the probability of the market being in the distressed regime are shown to giv...

A mixture of Student t-distributions (MoT) has been widely used to model multivariate data sets with atypical observations, or outliers for robust clustering. In this paper, we developed a novel robust clustering approach bymodeling the data sets usingmixture of Pearson type VII distributions (MoP). An EM algorithm is developed for the maximum likelihood estimation of the model parameters. An o...

We present a generic framework to track shapes across large variations by learning non-linear shape manifold as overlapping, piecewise linear subspaces. We use landmark based shape analysis to train a Gaussian mixture model over the aligned shapes and learn a Point Distribution Model(PDM) for each of the mixture components. The target shape is searched by first maximizing the mixture probabilit...

Mixture models provide a method of modeling a complex probability distribution in terms of simpler structures. In particular, the method of mixture of regressions has received considerable attention due to its modeling flexibility and availability of convenient computational algorithms. While the theoretical justification has been successfully worked out from the frequentist point of view, its ...