The usual robust optimality problem for given asGRisk searches the optimal clipping height b of a Hampel-type IC to given radius of the neighborhood. Instead, again for given asGRisk and for given Hampel-Type IC with given clipping height b we may determine the radius of the neighborhood for which it is optimal in the sense of the first sentence.
getRadius(IC, risk, neighbor, L2Fam)
The optimal radius is computed.
an IC of class "HampIC"
.
object of class "RiskType"
.
object of class "Neighborhood"
.
object of class "L2FamParameter"
. Can be missing;
in this case it is constructed from slot CallL2Fam
of
IC
.
Peter Ruckdeschel peter.ruckdeschel@uni-oldenburg.de
Rieder, H. (1980) Estimates derived from robust tests. Ann. Stats. 8: 106--115.
Rieder, H. (1994) Robust Asymptotic Statistics. New York: Springer.
Ruckdeschel, P. and Rieder, H. (2004) Optimal Influence Curves for General Loss Functions. Statistics & Decisions 22, 201-223.
Ruckdeschel, P. (2005) Optimally One-Sided Bounded Influence Curves. Mathematical Methods in Statistics 14(1), 105-131.
Kohl, M. (2005) Numerical Contributions to the Asymptotic Theory of Robustness. Bayreuth: Dissertation.
ContIC-class
, TotalVarIC-class
N <- NormLocationFamily(mean=0, sd=1)
nb <- ContNeighborhood(); ri <- asMSE()
radIC <- radiusMinimaxIC(L2Fam=N, neighbor=nb, risk=ri, loRad=0.1, upRad=0.5)
getRadius(radIC, L2Fam=N, neighbor=nb, risk=ri)
## taken from script NormalScaleModel.R in folder scripts
N0 <- NormScaleFamily(mean=0, sd=1)
(N0.IC7 <- radiusMinimaxIC(L2Fam=N0, neighbor=nb, risk=ri, loRad=0, upRad=Inf))
##
getRadius(N0.IC7, risk=asMSE(), neighbor=nb, L2Fam=N0)
getRadius(N0.IC7, risk=asL4(), neighbor=nb, L2Fam=N0)
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