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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Jun Jin & Tiefeng Ma & Jiajia Dai & Shuangzhe Liu, 2021. "Penalized weighted composite quantile regression for partially linear varying coefficient models with missing covariates," Computational Statistics, Springer, vol. 36(1), pages 541-575, March.
    • Handle: RePEc:spr:compst:v:36:y:2021:i:1:d:10.1007_s00180-020-01012-z
    DOI: 10.1007/s00180-020-01012-z
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    References listed on IDEAS

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