scholarly journals High‐dimensional quantile regression: Convolution smoothing and concave regularization

2021 ◽  
Author(s):  
Kean Ming Tan ◽  
Lan Wang ◽  
Wen‐Xin Zhou
Keyword(s):  
Quantile Regression ◽  
High Dimensional

Extensions to Quantile Regression Forests for Very High-Dimensional Data

2014 ◽  
pp. 247-258 ◽  
Author(s):  
Nguyen Thanh Tung ◽  
Joshua Zhexue Huang ◽  
Imran Khan ◽  
Mark Junjie Li ◽  
Graham Williams
Keyword(s):  
Quantile Regression ◽  
High Dimensional Data ◽  
High Dimensional ◽  
Very High

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

scholarly journals Valid Post-Selection Inference in High-Dimensional Approximately Sparse Quantile Regression Models

2018 ◽  
Vol 114 (526) ◽  
pp. 749-758 ◽  
Author(s):  
Alexandre Belloni ◽  
Victor Chernozhukov ◽  
Kengo Kato
Keyword(s):  
Quantile Regression ◽  
Regression Models ◽  
High Dimensional

Sparse signal shrinkage and outlier detection in high-dimensional quantile regression with variational Bayes

2020 ◽  
Vol 13 (2) ◽  
pp. 237-249 ◽  
Author(s):  
Daeyoung Lim ◽  
Beomjo Park ◽  
David Nott ◽  
Xueou Wang ◽  
Taeryon Choi
Keyword(s):  
Quantile Regression ◽  
Outlier Detection ◽  
Variational Bayes ◽  
High Dimensional ◽  
Sparse Signal

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.

Sign in / Sign up

Export Citation Format

Share Document