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.

在本文中，我们研究了缺少协变量的部分线性变化系数模型。基于逆概率加权和B样条近似，我们提出了一种加权B样条复合分位数回归方法来估计非参数函数和回归系数。在某些温和条件下，我们建立拟议估计量的渐近正态性和Horvitz-Thompson性质。我们将建议的估计方法与自适应LASSO相结合，进一步研究了变量选择程序。研究了所提出的变量选择方法的预言性。在缺少协变量的情况下，