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

2020 ◽  
Author(s):  
Jun Jin ◽  
Tiefeng Ma ◽  
Jiajia Dai ◽  
Shuangzhe Liu
Keyword(s):  
Quantile Regression ◽  
Missing Covariates ◽  
Varying Coefficient Models ◽  
Composite Quantile Regression ◽  
Varying Coefficient ◽  
Partially Linear

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.

scholarly journals Quantile regression in partially linear varying coefficient models

2009 ◽  
Vol 37 (6B) ◽  
pp. 3841-3866 ◽  
Author(s):  
Huixia Judy Wang ◽  
Zhongyi Zhu ◽  
Jianhui Zhou
Keyword(s):  
Quantile Regression ◽  
Varying Coefficient Models ◽  
Varying Coefficient ◽  
Partially Linear

Imputed Estimation For Varying Coefficient Models with Missing Covariates

2013 ◽  
Vol 787 ◽  
pp. 1089-1092
Author(s):  
Pei Xin Zhao
Keyword(s):  
Missing Data ◽  
Estimation Procedure ◽  
Estimating Equation ◽  
Equation Method ◽  
Missing Covariates ◽  
Varying Coefficient Models ◽  
Finite Sample ◽  
Varying Coefficient

By using the imputation-based estimating equation method, an imputed estimation procedure for the coefficient functions is proposed. The proposed procedure can attenuate the effect of the missing data, and performs well for the finite sample.

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