Name
Title | ||
|---|---|---|
Penalized Weighted Composite Quantile Regression For Partially Linear Varying Coefficient Models With Missing Covariates |
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. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1007/s00180-020-01012-z | COMPUTATIONAL STATISTICS |
Keywords | DocType | Volume |
Composite quantile regression, Horvitz-Thompson property, Missing at random, Partially linear varying coefficient | Journal | 36 |
Issue | ISSN | Citations |
1 | 0943-4062 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jin Jun | 1 | 2 | 4.82 |
| Tiefeng Ma | 2 | 7 | 3.47 |
| Jiajia Dai | 3 | 0 | 0.34 |
| Shuangzhe Liu | 4 | 6 | 4.21 |