Weighted composite quantile regression with censoring indicators missing at random

2019 ◽  
pp. 1-18
Author(s):  
Jiang-Feng Wang ◽  
Wei-Jun Jiang ◽  
Fang-Yin Xu ◽  
Wu-Xin Fu
Keyword(s):  
Quantile Regression ◽  
Missing At Random ◽  
Composite Quantile Regression

scholarly journals Composite Quantile Regression for Varying Coefficient Models with Response Data Missing at Random

Symmetry ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 1065 ◽  
Author(s):  
Shuanghua Luo ◽  
Cheng-yi Zhang ◽  
Meihua Wang
Keyword(s):  
Quantile Regression ◽  
Real Life ◽  
Missing At Random ◽  
Unknown Coefficient ◽  
Varying Coefficient Models ◽  
Test Procedures ◽  
Composite Quantile Regression ◽  
Varying Coefficient ◽  
Response Data ◽  
Data Missing

Composite quantile regression (CQR) estimation and inference are studied for varying coefficient models with response data missing at random. Three estimators including the weighted local linear CQR (WLLCQR) estimator, the nonparametric WLLCQR (NWLLCQR) estimator, and the imputed WLLCQR (IWLLCQR) estimator are proposed for unknown coefficient functions. Under some mild conditions, the proposed estimators are asymptotic normal. Simulation studies demonstrate that the unknown coefficient estimators with IWLLCQR are superior to the other two with WLLCQR and NWLLCQR. Moreover, bootstrap test procedures based on the IWLLCQR fittings is developed to test whether the coefficient functions are actually varying. Finally, a type of investigated real-life data is analyzed to illustrated the applications of the proposed method.

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