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

• Main Authors: Shuanghua Luo, Cheng-yi Zhang, Meihua Wang
• Format: Article
• Language: English
• Published: MDPI AG 2019-08-01
• Series: Symmetry
• Subjects: varying coefficient model; composite quantile regression; missing at random; inverse probability weighting; imputed method
• Online Access: https://www.mdpi.com/2073-8994/11/9/1065

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.