Variable selection in high-dimensional partially linear additive models for composite quantile regression

2013 ◽  
Vol 65 ◽  
pp. 56-67 ◽  
Author(s):  
Jie Guo ◽  
Manlai Tang ◽  
Maozai Tian ◽  
Kai Zhu
Keyword(s):  
Variable Selection ◽  
Quantile Regression ◽  
Additive Models ◽  
High Dimensional ◽  
Composite Quantile Regression ◽  
Partially Linear

scholarly journals Variable Selection via SCAD-Penalized Quantile Regression for High-Dimensional Count Data

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 153205-153216
Author(s):  
Dost Muhammad Khan ◽  
Anum Yaqoob ◽  
Nadeem Iqbal ◽  
Abdul Wahid ◽  
Umair Khalil ◽  
...  
Keyword(s):  
Variable Selection ◽  
Quantile Regression ◽  
Count Data ◽  
High Dimensional

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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