Variable selection in finite mixture of semi-parametric regression models

Ehsan Ormoz and Farzad Eskandari

Communications in Statistics - Theory and Methods, 2016, vol. 45, issue 3, 695-711

Abstract: In this paper we are concerned with variable selection in finite mixture of semiparametric regression models. This task consists of model selection for non parametric component and variable selection for parametric part. Thus, we encountered separate model selections for every non parametric component of each sub model. To overcome this computational burden, we introduced a class of variable selection procedures for finite mixture of semiparametric regression models using penalized approach for variable selection. It is shown that the new method is consistent for variable selection. Simulations show that the performance of proposed method is good, and it consequently improves pervious works in this area and also requires much less computing power than existing methods.

Date: 2016
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DOI: 10.1080/03610926.2013.835413

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