Insurance claim severity data are characterized by complex distributional phenomenons, where flexible density estimation tools such as the finite mixture models (FMM) necessary. However, maximum likelihood estimations (MLE) often produce unstable tail estimates for FMM. Motivated this challenge, article presents a weighted estimator (MWLE) robust of heavy-tailed Under some regularity conditions...

Finite mixture models are statistical models which appear in many problems in statistics and machine learning. In such models it is assumed that data are drawn from random probability measures, called mixture components, which are themselves drawn from a probability measure P over probability measures. When estimating mixture models, it is common to make assumptions on the mixture components, s...

We discuss a flexible regression model for multivariate mixed responses. Dependence between outcomes is introduced via the joint distribution of discrete outcome- and individual-specific random effects that represent potential unobserved heterogeneity in each outcome profile. A different number locations can be used margin, association structure described by tensor further simplified using Para...

Modeling across site variation of the substitution process is increasingly recognized as important for obtaining more accurate phylogenetic reconstructions. Both finite and infinite mixture models have been proposed and have been shown to significantly improve on classical single-matrix models. Compared with their finite counterparts, infinite mixtures have a greater expressivity. However, they...

The consistent estimation of mixture complexity is of fundamental importance in many applications of finite mixture models. An enormous body of literature exists regarding the application, computational issues and theoretical aspects of mixture models when the number of components is known, but estimating the unknown number of components remains an area of intense research effort. This article ...

Finite mixture multivariate generalized linear modeling has been shown to be an important analytic tool for many research fields, for example, image recognition, astronomical data classification, biomedicine diagnosis, and biological classification. Recent statistical and computational advances have further encouraged researchers to explore the modeling possibility using the Bayesian framework....

We propose a method for approximating integrated likelihoods in finite mixture models. We formulate the model in terms of the unobserved group memberships, z, and make them the variables of integration. The integral is then evaluated using importance sampling over the z. We propose an adaptive importance sampling function which is itself a mixture, with two types of component distributions, one...