A new variable selection approach for varying coefficient models

Xue-Jun Ma and Jing-Xiao Zhang ()

Metrika: International Journal for Theoretical and Applied Statistics, 2016, vol. 79, issue 1, 59-72

Abstract: The varying coefficient models are very important tools to explore the hidden structure between the response variable and its predictors. However, variable selection and identification of varying coefficients of the models are poorly understood. In this paper, we develop a novel method to overcome these difficulties using local polynomial smoothing and the SCAD penalty. Under some regularity conditions, we show that the proposed procedure is consistent in separating the varying coefficients from the constant ones. The resulting estimator can be as efficient as the oracle. Simulation results confirm our theories. Finally, we study the Boston housing data using the proposed method. Copyright Springer-Verlag Berlin Heidelberg 2016

Keywords: Varying coefficient models; Variable selection; SCAD; Oracle property; 62G08 (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:spr:metrik:v:79:y:2016:i:1:p:59-72

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DOI: 10.1007/s00184-015-0543-y

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