Penalized weighted composite quantile regression for partially linear varying coefficient models with missing covariates

Jun Jin (), Tiefeng Ma, Jiajia Dai and Shuangzhe Liu
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Jun Jin: Southwestern University of Finance and Economics
Tiefeng Ma: Southwestern University of Finance and Economics
Jiajia Dai: Guizhou University
Shuangzhe Liu: University of Canberra

Computational Statistics, 2021, vol. 36, issue 1, No 23, 575 pages

Abstract: Abstract In this paper we study partially linear varying coefficient models with missing covariates. Based on inverse probability-weighting and B-spline approximations, we propose a weighted B-spline composite quantile regression method to estimate the non-parametric function and the regression coefficients. Under some mild conditions, we establish the asymptotic normality and Horvitz–Thompson property of the proposed estimators. We further investigate a variable selection procedure by combining the proposed estimation method with adaptive LASSO. The oracle property of the proposed variable selection method is studied. Under a missing covariate scenario, two simulations with various non-normal error distributions and a real data application are conducted to assess and showcase the finite sample performance of the proposed estimation and variable selection methods.

Keywords: Composite quantile regression; Horvitz–Thompson property; Missing at random; Partially linear varying coefficient (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s00180-020-01012-z

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