Robust variable selection in high-dimensional varying coefficient models based on weighted composite quantile regression

2015 ◽  
Vol 58 (4) ◽  
pp. 1009-1033 ◽  
Author(s):  
Chaohui Guo ◽  
Hu Yang ◽  
Jing Lv
2012 ◽  
Vol 6 (0) ◽  
pp. 1220-1238 ◽  
Author(s):  
Hohsuk Noh ◽  
Kwanghun Chung ◽  
Ingrid Van Keilegom

Symmetry ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 1065 ◽  
Author(s):  
Shuanghua Luo ◽  
Cheng-yi Zhang ◽  
Meihua Wang

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.


2013 ◽  
Vol 122 ◽  
pp. 115-132 ◽  
Author(s):  
Yanlin Tang ◽  
Xinyuan Song ◽  
Huixia Judy Wang ◽  
Zhongyi Zhu

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