Model selection for probabilistic clustering using cross-validatedlikelihood
Statistics and Computing
Fitting of mixtures with unspecified number of components using cross validation distance estimate
Computational Statistics & Data Analysis
A stochastic EM algorithm for a semiparametric mixture model
Computational Statistics & Data Analysis
Maximum likelihood computation for fitting semiparametric mixture models
Statistics and Computing
Bayesian semiparametric modeling and inference with mixtures of symmetric distributions
Statistics and Computing
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It may sometimes be clear from background knowledge that a population under investigation proportionally consists of a known number of subpopulations, whose distributions belong to the same, yet unknown, family. While a parametric family is commonly used in practice, one can also consider some nonparametric families to avoid distributional misspecification. In this article, we propose a solution using a mixture-based nonparametric family for the component distribution in a finite mixture model as opposed to some recent research that utilizes a kernel-based approach. In particular, we present a semiparametric maximum likelihood estimation procedure for the model parameters and tackle the bandwidth parameter selection problem via some popular means for model selection. Empirical comparisons through simulation studies and three real data sets suggest that estimators based on our mixture-based approach are more efficient than those based on the kernel-based approach, in terms of both parameter estimation and overall density estimation.