scholarly journals Variable selection for additive partial linear quantile regression with missing covariates

2016 ◽  
Vol 152 ◽  
pp. 206-223 ◽  
Author(s):  
Ben Sherwood
Keyword(s):  
Variable Selection ◽  
Quantile Regression ◽  
Missing Covariates ◽  
Selection For ◽  
Partial Linear

scholarly journals Variable selection for non-parametric quantile regression via smoothing spline analysis of variance

Stat ◽  
2013 ◽  
Vol 2 (1) ◽  
pp. 255-268 ◽  
Author(s):  
Chen-Yen Lin ◽  
Howard Bondell ◽  
Hao Helen Zhang ◽  
Hui Zou
Keyword(s):  
Variable Selection ◽  
Quantile Regression ◽  
Analysis Of Variance ◽  
Smoothing Spline ◽  
Selection For ◽  
Non Parametric

Variable selection for ultra-high dimensional quantile regression with missing data and measurement error

2020 ◽  
pp. 096228022094153
Author(s):  
Yongxin Bai ◽  
Maozai Tian ◽  
Man-Lai Tang ◽  
Wing-Yan Lee
Keyword(s):  
Missing Data ◽  
Measurement Error ◽  
Variable Selection ◽  
Quantile Regression ◽  
Measurement Errors ◽  
Estimation Method ◽  
High Dimensional ◽  
Data Set ◽  
Cancer Data ◽  
Selection For

In this paper, we consider variable selection for ultra-high dimensional quantile regression model with missing data and measurement errors in covariates. Specifically, we correct the bias in the loss function caused by measurement error by applying the orthogonal quantile regression approach and remove the bias caused by missing data using the inverse probability weighting. A nonconvex Atan penalized estimation method is proposed for simultaneous variable selection and estimation. With the proper choice of the regularization parameter and under some relaxed conditions, we show that the proposed estimate enjoys the oracle properties. The choice of smoothing parameters is also discussed. The performance of the proposed variable selection procedure is assessed by Monte Carlo simulation studies. We further demonstrate the proposed procedure with a breast cancer data set.

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