Finite mixture models are being increasingly used in statistical inference and to provide a model-based approach to cluster analysis. Mixture models can be fitted to independent data in a straightforward manner via the expectation-maximization (EM) algorithm. In this paper, we look at ways of speeding up the fitting of normal mixture models by using variants of the EM, including the so-called s...

The rebmix package for fitting finite mixture models implemented in R package rebmix is presented. It provides functions for random univariate and multivariate finite mixture generation, the number of components, component weights and component parameter estimation, bootstrapping and the plotting of finite mixtures. It requires preprocessing of observations, information criterion and conditiona...

One of the greatest challenges in application finite mixture models is model comparison. A variety statistical fit indices exist, including information criteria, approximate likelihood ratio tests, and resampling techniques; however, none these describe amount improvement when a latent class added to model. We review statistics propose novel approach, increment percentage per parameter ( LIPpp ...