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MTE (version 1.0.0)

Maximum Tangent Likelihood and Other Robust Estimation for High-Dimensional Regression

Description

Provides several robust estimators for linear regression and variable selection. They are Maximum tangent likelihood estimator (Qin, et al. (2017) ), least absolute deviance estimator, and Huber loss. 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.

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Version

Install

install.packages('MTE')

Monthly Downloads

739

Version

1.0.0

License

GPL-3

Maintainer

Shaobo Li

Last Published

August 29th, 2017

Functions in MTE (1.0.0)

huber.reg

Huber estimation for linear regression
huberloss

Huber Loss
lnt

Tangent-likelihood function.
der.LtLik

Derivatives of Tangent Likelihood w.r.t. beta
huber.lasso

Huber-Lasso estimator
MTE

Maximum Tangent-likelihood Estimation
MTElasso

MTE-Lasso estimator
LAD

Least Absolute Deviance Estimator for Linear Regression
LADlasso

LAD-Lasso for Linear Regression