Editorial: Advances in Mixture Models
Computational Statistics & Data Analysis
Bayesian semiparametric modeling and inference with mixtures of symmetric distributions
Statistics and Computing
Model based labeling for mixture models
Statistics and Computing
Estimation of finite mixtures with symmetric components
Statistics and Computing
Computational Statistics & Data Analysis
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Recently, there has been a considerable interest in finite mixture models with semi-/non-parametric component distributions. Identifiability of such model parameters is generally not obvious, and when it occurs, inference methods are rather specific to the mixture model under consideration. Hence, a generalization of the EM algorithm to semiparametric mixture models is proposed. The approach is methodological and can be applied to a wide class of semiparametric mixture models. The behavior of the proposed EM type estimators is studied numerically not only through several Monte-Carlo experiments but also through comparison with alternative methods existing in the literature. In addition to these numerical experiments, applications to real data are provided, showing that the estimation method behaves well, that it is fast and easy to be implemented.