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

Jun Jin, Tiefeng Ma, Jiajia Dai, Shuangzhe Liu

Research output: Contribution to journalArticle

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.

Original languageEnglish
Pages (from-to)1-35
Number of pages35
JournalComputational Statistics
DOIs
Publication statusE-pub ahead of print - 9 Jul 2020

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