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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
).
devtools::install_github("shaobo-li/MTE")
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
Qin, Y., Li, S., Li, Y., & Yu, Y. (2017). Penalized maximum tangent likelihood estimation and robust variable selection. arXiv:1708.05439.
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