rlsOptIC.HuMad: Computation of the optimally robust IC for HuMad estimators

In RobLox: Optimally Robust Influence Curves and Estimators for Location and Scale

Computation of the optimally robust IC for HuMad estimators

Description

The function rlsOptIC.HuMad computes the optimally robust IC for HuMad estimators in case of normal location with unknown scale and (convex) contamination neighborhoods. These estimators were proposed by Andrews et al. (1972), p. 12. A definition of these estimators can also be found in Subsection 8.5.1 of Kohl (2005).

Usage

rlsOptIC.HuMad(r, kUp = 2.5, delta = 1e-06)

Arguments

r	non-negative real: neighborhood radius.
kUp	positive real: the upper end point of the interval to be searched for k.
delta	the desired accuracy (convergence tolerance).

Details

The optimal value of the tuning constant k can be read off from the slot Infos of the resulting IC.

Value

Object of class "IC"

Author(s)

Matthias Kohl [e-mail]

References

Andrews, D.F., Bickel, P.J., Hampel, F.R., Huber, P.J., Rogers, W.H. and Tukey, J.W. (1972) Robust estimates of location. Princeton University Press.

M. Kohl (2005). Numerical Contributions to the Asymptotic Theory of Robustness. Dissertation. University of Bayreuth. https://epub.uni-bayreuth.de/id/eprint/839/2/DissMKohl.pdf.

See Also

IC-class

Examples

IC1 <- rlsOptIC.HuMad(r = 0.1)
checkIC(IC1)
Risks(IC1)
Infos(IC1)
plot(IC1)
infoPlot(IC1)

RobLox documentation built on Jan. 12, 2025, 3 a.m.