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MTE  

Maximum Tangent Likelihood Estimation for Robust Linear Regression and Variable Selection
View on CRAN: Click here


Download and install MTE package within the R console
Install from CRAN:
install.packages("MTE")

Install from Github:
library("remotes")
install_github("cran/MTE")

Install by package version:
library("remotes")
install_version("MTE", "1.2")



Attach the package and use:
library("MTE")
Maintained by
Shaobo Li
[Scholar Profile | Author Map]
First Published: 2017-08-29
Latest Update: 2023-04-11
Description:
Several robust estimators for linear regression and variable selection are provided. Included are Maximum tangent likelihood estimator by Qin, et al., (2017) , 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.
How to cite:
Shaobo Li (2017). MTE: Maximum Tangent Likelihood Estimation for Robust Linear Regression and Variable Selection. R package version 1.2, https://cran.r-project.org/web/packages/MTE. Accessed 16 Apr. 2025.
Previous versions and publish date:
1.0.0 (2017-08-29 14:18), 1.0.1 (2021-05-11 11:12), 1.0.2 (2022-03-22 19:20)
Other packages that cited MTE R package
View MTE citation profile
Other R packages that MTE depends, imports, suggests or enhances
Complete documentation for MTE
Functions, R codes and Examples using the MTE R package
Some associated functions: LAD . LADlasso . MTE . MTElasso . huber.lasso . huber.reg . huberloss . 
Some associated R codes: Huber.R . LAD.R . MTE.R .  Full MTE package functions and examples
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