Hidden Markov Models can be considered an extension of mixture models, allowing for dependent observations. In a hierarchical Bayesian framework, we show how Reversible Jump Markov Chain Monte Carlo techniques can be used to estimate the parameters of a model, as well as the number of regimes. We consider a mixture of normal distributions characterized by different means and variances under eac...

A theorem due to L. J. Gleser stating that a gamma distribution with shape parameter no greater than one is a mixed exponential distribution is extended to generalized gamma distributions introduced by E. W. Stacy as a special family of lifetime distributions containing both gamma distributions, exponential power and Weibull distributions. It is shown that the mixing distribution is a scale mix...

This article introduces a bimodal model based on the epsilon-skew-normal distribution. extension generates distributions similar to those produced by mixture of normal distributions. We study basic properties this new family. apply maximum likelihood estimators, calculate information matrix and present simulation assess parameter recovery. Finally, we illustrate results three real data sets, su...

background & aim: in the survival data with long-term survivors the event has not occurred for all the patients despite long-term follow-up, so the survival time for a certain percent is censored at the  end  of the  study.  mixture  cure  model  was  introduced  by boag,  1949  for  reaching  a  more efficient analysis of this set of data. because of some disadvantages of this model non-mixtur...

Finite mixture models are being increasingly used to model the distributions of a wide variety of random phenomena and to cluster data sets. In this paper, we focus on the use of normal mixture models to cluster data sets of continuous multivariate data. As normality based methods of estimation are not robust, we review the use of t component distributions. With the t mixture model-based approa...

Abstract Although there are many papers on variable selection methods based mean model in the finite mixture of regression models, little work has been done how to select significant explanatory variables modeling variance parameter. In this paper, we propose and study a novel class models: skew-normal joint location scale models analyze heteroscedastic data coming from heterogeneous population...