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)
Value
Returns Huber's M-estimator of location.
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.
Author
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.