Computation of the optimally robust IC for MM2 estimators
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.
Kohl, M. (2005) Numerical Contributions to the Asymptotic Theory of Robustness. Bayreuth: Dissertation.
IC1 <- rlsOptIC.MM2(r = 0.1) checkIC(IC1) Risks(IC1) Infos(IC1) plot(IC1) infoPlot(IC1)
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