huber.reg: Huber estimation for linear regression

In MTE: Maximum Tangent Likelihood Estimation for Robust Linear Regression and Variable Selection

Huber estimation for linear regression

Description

This function produces Huber estimates for linear regression. Initial estimates is required. Currently, the function does not support automatic selection of huber tuning parameter.

Usage

huber.reg(y, X, beta.ini, alpha, intercept = FALSE)

Arguments

y	the response vector
X	design matrix
beta.ini	initial value of estimates, could be from OLS.
alpha	1/alpha is the huber tuning parameter delta. Larger alpha results in smaller portion of squared loss.
intercept	logical input that indicates if intercept needs to be estimated. Default is FALSE.

Value

beta	the regression coefficient estimates
fitted.value	predicted response
iter.steps	iteration steps.

Examples

set.seed(2017)
n=200; d=4
X=matrix(rnorm(n*d), nrow=n, ncol=d)
beta=c(1, -1, 2, -2)
y=-2+X%*%beta+c(rnorm(150), rnorm(30,10,10), rnorm(20,0,100))
beta0=beta.ls=lm(y~X)$coeff
beta.huber=huber.reg(y, X, beta0, 2, intercept=TRUE)$beta
cbind(c(-2,beta), beta.ls, beta.huber)

MTE documentation built on April 11, 2023, 6:11 p.m.