huber.mu: Huber M-estimator of location

In asbio: A Collection of Statistical Tools for Biologists

Huber M-estimator of location

Description

The Huber M-estimator is a robust high efficiency estimator of location that has probably been under-utilized by biologists. It is based on maximizing the likelihood of a weighting function. This is accomplished using an iterative least squares process. The Newton Raphson algorithm is used here. The function usually converges fairly quickly (< 10 iterations). The function uses the Median Absolute Deviation function, mad. Note that if MAD = 0, then NA is returned.

Usage

huber.mu(x, c = 1.28, iter = 20, conv = 1e-07)

Arguments

x	A vector of quantitative data.
c	Stop criterion. The value c = 1.28 gives 95% efficiency of the mean given normality.
iter	Maximum number of iterations.
conv	Convergence criterion.

Value

Returns Huber's M-estimator of location.

Author(s)

Ken Aho

References

Huber, P. J. (2004) Robust Statistics. Wiley.

Wilcox, R. R. (2005) Introduction to Robust Estimation and Hypothesis Testing, Second Edition. Elsevier, Burlington, MA.

See Also

huber.one.step, huber.NR, mad

Examples

x <- rnorm(100)
huber.mu(x)

asbio documentation built on Aug. 20, 2023, 9:07 a.m.