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)
n=200; d=50
X=matrix(rnorm(n*d), nrow=n, ncol=d)
beta=c(rep(2,6), rep(0, 44))
y=X%*%beta+c(rnorm(150), rnorm(30,10,10), rnorm(20,0,100))
beta0=MTE(y, X, rep(0,50), 0.1, 2)$beta
output.MTELasso=MTElasso(y,X, p=2, beta.ini=beta0, t=seq(0, 0.1, 0.01), method="MTE")
beta.est=output.MTELasso$beta

References

Qin, Y., Li, S., Li, Y., & Yu, Y. (2017). Penalized maximum tangent likelihood estimation and robust variable selection. arXiv:1708.05439.



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MTE documentation built on March 23, 2022, 1:07 a.m.