Automatic structure discovery for varying-coefficient partially linear models

Guangren Yang, Yanqing Sun and Xia Cui

Communications in Statistics - Theory and Methods, 2017, vol. 46, issue 15, 7703-7716

Abstract: Varying-coefficient partially linear models provide a useful tools for modeling of covariate effects on the response variable in regression. One key question in varying-coefficient partially linear models is the choice of model structure, that is, how to decide which covariates have linear effect and which have non linear effect. In this article, we propose a profile method for identifying the covariates with linear effect or non linear effect. Our proposed method is a penalized regression approach based on group minimax concave penalty. Under suitable conditions, we show that the proposed method can correctly determine which covariates have a linear effect and which do not with high probability. The convergence rate of the linear estimator is established as well as the asymptotical normality. The performance of the proposed method is evaluated through a simulation study which supports our theoretical results.

Date: 2017
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DOI: 10.1080/03610926.2016.1161796

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