An EM algorithm for fitting a mixture model with symmetric log-concave densities

Xiao Pu and Ery Arias-Castro

Communications in Statistics - Theory and Methods, 2020, vol. 49, issue 1, 78-87

Abstract: In this article, we revisit the problem of fitting a mixture model under the assumption that the mixture components are symmetric and log-concave. To this end, we first study the nonparametric maximum likelihood estimation (MLE) of a monotone log-concave probability density. To fit the mixture model, we propose a semiparametric EM (SEM) algorithm, which can be adapted to other semiparametric mixture models. In our numerical experiments, we compare our algorithm to that of Balabdaoui and Doss (2018, Inference for a two-component mixture of symmetric distributions under log-concavity. Bernoulli 24 (2):1053–71) and other mixture models both on simulated and real-world datasets.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:taf:lstaxx:v:49:y:2020:i:1:p:78-87

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DOI: 10.1080/03610926.2018.1530789

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