hrqglas - Group Variable Selection for Quantile and Robust Mean Regression
A program that conducts group variable selection for quantile and robust mean regression (Sherwood and Li, 2022). The group lasso penalty (Yuan and Lin, 2006) is used for group-wise variable selection. Both of the quantile and mean regression models are based on the Huber loss. Specifically, with the tuning parameter in the Huber loss approaching to 0, the quantile check function can be approximated by the Huber loss for the median and the tilted version of Huber loss at other quantiles. Such approximation provides computational efficiency and stability, and has also been shown to be statistical consistent.
Last updated 2 years ago
quantileregressionvariable-selection
4.26 score 3 stars 4 dependents 8 scripts 441 downloadsMTE - Maximum Tangent Likelihood Estimation for Robust Linear Regression and Variable Selection
Several robust estimators for linear regression and variable selection are provided. Included are Maximum tangent likelihood estimator by Qin, et al., (2017) <arxiv:1708.05439>, least absolute deviance estimator and Huber regression. The penalized version of each of these estimator incorporates L1 penalty function, i.e., LASSO and Adaptive Lasso. They are able to produce consistent estimates for both fixed and high-dimensional settings.
Last updated 2 years ago
3.70 score 1 stars 8 scripts 596 downloads