| rlsOptIC.Hu2a {RobLox} | R Documentation |
Computation of the optimally robust IC for Hu2a estimators
Description
The function rlsOptIC.Hu2a computes the optimally robust IC for
Hu2a estimators in case of normal location with unknown scale and
(convex) contamination neighborhoods. These estimators are a
simple modification of Huber (1964), Proposal 2 where we, in addition,
admit a clipping from below. The definition of
these estimators can be found in Subsection 8.5.1 of Kohl (2005).
Usage
rlsOptIC.Hu2a(r, k1.start = 0.25, k2.start = 2.5, delta = 1e-06, MAX = 100)
Arguments
r |
non-negative real: neighborhood radius. |
k1.start |
positive real: starting value for k1. |
k2.start |
positive real: starting value for k2. |
delta |
the desired accuracy (convergence tolerance). |
MAX |
if k1 or k2 are beyond the admitted values,
|
Details
The computation of the optimally robust IC for Hu2a estimators
is based on optim where MAX is used to
control the constraints on k1 and k2. The optimal values of
the tuning constants k1 and k2 can be read off
from the slot Infos of the resulting IC.
Value
Object of class "IC"
Author(s)
Matthias Kohl Matthias.Kohl@stamats.de
References
Huber, P.J. (1964) Robust estimation of a location parameter. Ann. Math. Stat. 35: 73–101.
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
Examples
IC1 <- rlsOptIC.Hu2a(r = 0.1)
checkIC(IC1)
Risks(IC1)
Infos(IC1)
plot(IC1)
infoPlot(IC1)