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The function rlsOptIC.Hu2 computes the optimally robust IC for
Hu2 estimators in case of normal location with unknown scale and
(convex) contamination neighborhoods. These estimators were
proposed in Example 6.4.1 of Huber (1981). A definition of these
estimators can also be found in Subsection 8.5.1 of Kohl (2005).
rlsOptIC.Hu2(r, k.start = 1.5, c.start = 1.5, delta = 1e-06, MAX = 100)Object of class "IC"
non-negative real: neighborhood radius.
positive real: starting value for k.
positive real: starting value for c.
the desired accuracy (convergence tolerance).
if k1 or k2 are beyond the admitted values,
MAX is returned.
Matthias Kohl Matthias.Kohl@stamats.de
The computation of the optimally robust IC for Hu2 estimators
is based on optim where MAX is used to
control the constraints on k and c. The optimal values of
the tuning constants k and c can be read off
from the slot Infos of the resulting IC.
Huber, P.J. (1981) Robust Statistics. New York: Wiley.
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.
IC-class
IC1 <- rlsOptIC.Hu2(r = 0.1)
checkIC(IC1)
Risks(IC1)
Infos(IC1)
plot(IC1)
infoPlot(IC1)
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