Finite Mixture of Generalized Semiparametric Models: Variable Selection via Penalized Estimation |
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Authors: | Farzad Eskandari Ehsan Ormoz |
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Affiliation: | 1. Department of Statistics, Allameh Tabatabai University, Tehran, Iran;2. Department of Statistics, Mashhad Branch, Islamic Azad University, Mashhad, Iran |
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Abstract: | Selection of the important variables is one of the most important model selection problems in statistical applications. In this article, we address variable selection in finite mixture of generalized semiparametric models. To overcome computational burden, we introduce a class of variable selection procedures for finite mixture of generalized semiparametric models using penalized approach for variable selection. Estimation of nonparametric component will be done via multivariate kernel regression. It is shown that the new method is consistent for variable selection and the performance of proposed method will be assessed via simulation. |
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Keywords: | EM algorithm Finite mixture model LASSO Penalized likelihood SCAD Semiparametric models Variable selection |
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