ridge.m: Ridge Regression M-Estimator with Huber's psi

In abnormally-distributed/cvreg: Cross Validation and Robust Estimation Utilities

Description

This uses Huber's psi to estimate a robust least squares fit. This is a simple M-estimator. Returned standard errors are based on the Huber-White HC4-modified method of computing robust standard errors. Note, however, that the ridge estimator induces extra precision into the estimates on account of biasing them towards zero, so the resulting standard errors are potentially misleading.

Usage

ridge.m(formula, data, lambda = NULL, k = 2 * sqrt((ncol(x) + 1)/nrow(x)))

Arguments

formula	model formula
data	data frame
k	tuning constant for outlier downweighting. defaults to 2*sqrt(p/n).

Value

a list

References

Cribari-Neto F., Da Silva W.B. (2011). A New Heteroskedasticity-Consistent Covariance Matrix Estimator for the Linear Regression Model. Advances in Statistical Analysis, 95(2), 129–146.

abnormally-distributed/cvreg documentation built on May 3, 2020, 3:45 p.m.