MTE: 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) <doi:10.48550/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.

Version: 1.2
Depends: R (≥ 3.1.0)
Imports: stats, quantreg, glmnet, rqPen
Published: 2023-04-11
Author: Shaobo Li [aut, cre], Yichen Qin [aut]
Maintainer: Shaobo Li <shaobo.li at ku.edu>
License: GPL-3
URL: GitHub: https://github.com/shaobo-li/MTE
NeedsCompilation: no
Materials: README
CRAN checks: MTE results

Documentation:

Reference manual: MTE.pdf

Downloads:

Package source: MTE_1.2.tar.gz
Windows binaries: r-devel: MTE_1.2.zip, r-release: MTE_1.2.zip, r-oldrel: MTE_1.2.zip
macOS binaries: r-release (arm64): MTE_1.2.tgz, r-oldrel (arm64): MTE_1.2.tgz, r-release (x86_64): MTE_1.2.tgz
Old sources: MTE archive

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