Description
Maximum Tangent Likelihood Estimation for Robust Linear Regression and Variable Selection.
Description
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.
README.md
MTE: Maximum Tangent Likelihood Estimation
Overview
The package provides several robust estimation methods for linear regression under both fixed and high dimesional settings. The methods include Maximum Tangent Likelihood Estimator (MTE and MTElasso) (Qin et al., 2017+), Least Absolute Deviance Estimator (LAD and LADlasso) and Huber estimator (huber.reg and huber.lasso).
Installation
devtools::install_github("shaobo-li/MTE")
Example
library(MTE)
set.seed(2017)
n=200; d=500
X=matrix(rnorm(n*d), nrow=n, ncol=d)
beta=c(rep(2,6), rep(0, d-6))
y=X%*%beta+c(rnorm(150), rnorm(30,10,10), rnorm(20,0,100))
output.MTELasso=MTElasso(X, y, p=2, t=0.01)
beta.est=output.MTELasso$beta
References
Qin, Y., Li, S., Li, Y., & Yu, Y. (2017). Penalized maximum tangent likelihood estimation and robust variable selection. arXiv:1708.05439.