rlsOptIC.MM2 | R Documentation |
The function rlsOptIC.MM2
computes the optimally robust IC for
MM2 estimators in case of normal location with unknown scale and
(convex) contamination neighborhoods. These estimators are based
on a proposal of Fraiman et al. (2001), p. 206. A definition of
these estimators can also be found in Section 8.6 of Kohl (2005).
rlsOptIC.MM2(r, c.start = 1.5, d.start = 2, delta = 1e-06, MAX = 100)
r |
non-negative real: neighborhood radius. |
c.start |
positive real: starting value for c. |
d.start |
positive real: starting value for d. |
delta |
the desired accuracy (convergence tolerance). |
MAX |
if a or k are beyond the admitted values,
|
The computation of the optimally robust IC for MM2 estimators
is based on optim
where MAX
is used to
control the constraints on c and d. The optimal values of
the tuning constants c and d can be read off from the slot
Infos
of the resulting IC.
Object of class "IC"
Matthias Kohl Matthias.Kohl@stamats.de
Fraiman, R., Yohai, V.J. and Zamar, R.H. (2001) Optimal robust M-estimates of location. Ann. Stat. 29(1): 194-223.
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.MM2(r = 0.1)
checkIC(IC1)
Risks(IC1)
Infos(IC1)
plot(IC1)
infoPlot(IC1)
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