Estimation and testing for semiparametric mixtures of partially linear models

Xing Wu and Tian Liu

Communications in Statistics - Theory and Methods, 2017, vol. 46, issue 17, 8690-8705

Abstract: In this paper, we study the estimation and inference for a class of semiparametric mixtures of partially linear models. We prove that the proposed models are identifiable under mild conditions, and then give a PL–EM algorithm estimation procedure based on profile likelihood. The asymptotic properties for the resulting estimators and the ascent property of the PL–EM algorithm are investigated. Furthermore, we develop a test statistic for testing whether the non parametric component has a linear structure. Monte Carlo simulations and a real data application highlight the interest of the proposed procedures.

Date: 2017
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DOI: 10.1080/03610926.2016.1189569

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