adaHuber.mean: Adaptive Huber Mean Estimation

In adaHuber: Adaptive Huber Estimation and Regression

Adaptive Huber Mean Estimation

Description

Adaptive Huber mean estimator from a data sample, with robustification parameter τ determined by a tuning-free principle.

Usage

adaHuber.mean(X, epsilon = 1e-04, iteMax = 500)

Arguments

X	An n-dimensional data vector.
epsilon	(optional) The tolerance level in the iterative estimation procedure, iteration will stop when |μ_new - μ_old| < ε. The defalut value is 1e-4.
iteMax	(optional) Maximum number of iterations. Default is 500.

Value

A list including the following terms will be returned:

mu

The Huber mean estimator.

tau

The robustness parameter determined by the tuning-free principle.

iteration

The number of iterations in the estimation procedure.

References

Huber, P. J. (1964). Robust estimation of a location parameter. Ann. Math. Statist., 35, 73–101.

Wang, L., Zheng, C., Zhou, W. and Zhou, W.-X. (2021). A new principle for tuning-free Huber regression. Stat. Sinica, 31, 2153-2177.

Examples

n = 1000
mu = 2
X = rt(n, 2) + mu
fit.mean = adaHuber.mean(X)
fit.mean$mu

adaHuber documentation built on March 18, 2022, 5:41 p.m.