We present the R package mixsmsn, which implements routines for maximum likelihood
estimation (via an expectation maximization EM-type algorithm) in finite mixture models with …

Modeling based on finite mixture distributions is a rapidly developing area with an exploding
range of applications. Finite mixture models are nowadays applied in such diverse areas as …

In this paper we consider a flexible class of models, with elements that are finite mixtures of
multivariate skew-normal independent distributions. A general EM-type algorithm is …

Scale mixtures of normal distributions are often used as a challenging class for statistical
procedures for symmetrical data. In this article, we have defined a skewed version of these …

A flexible class of probability distributions, convenient for modeling data with skewness
behavior, discrepant observations and population heterogeneity is presented. The elements …

Non-normal mixture distributions have received increasing attention in recent years. Finite
mixtures of multivariate skew-symmetric distributions, in particular, the skew normal and …

The em algorithm can be used to compute maximum likelihood estimates of model
parameters for skew-t mixture models. We show that the intractable expectations needed in …

Finite mixtures of skew distributions have emerged as an effective tool in modelling
heterogeneous data with asymmetric features. With various proposals appearing rapidly in …

This paper provides a flexible mixture modeling framework using the multivariate skew
normal distribution. A feasible EM algorithm is developed for finding the maximum likelihood …

This paper presents a robust probabilistic mixture model based on the multivariate skew-t-
normal distribution, a skew extension of the multivariate Student'st distribution with more …