getInfClip: Generic Function for the Computation of the Optimal Clipping Bound

Description

Generic function for the computation of the optimal clipping bound in case of infinitesimal robust models. This function is rarely called directly. It is used to compute optimally robust ICs.

Usage

getInfClip(clip, L2deriv, risk, neighbor, ...)
# S4 method for numeric,UnivariateDistribution,asMSE,ContNeighborhood
getInfClip(clip, L2deriv, risk, neighbor, cent, symm, trafo)
# S4 method for numeric,UnivariateDistribution,asMSE,TotalVarNeighborhood
getInfClip(clip, L2deriv, risk, neighbor, cent, symm, trafo)
# S4 method for numeric,EuclRandVariable,asMSE,ContNeighborhood
getInfClip(clip, L2deriv, risk, neighbor, Distr, stand, cent, trafo)
# S4 method for numeric,UnivariateDistribution,asUnOvShoot,UncondNeighborhood
getInfClip(clip, L2deriv, risk, neighbor, cent, symm, trafo)

Arguments

clip

positive real: clipping bound

L2deriv

L2-derivative of some L2-differentiable family of probability measures.

risk

object of class "RiskType".

neighbor

object of class "Neighborhood".

…

additional parameters.

cent

optimal centering constant.

stand

standardizing matrix.

Distr

object of class "Distribution".

symm

logical: indicating symmetry of L2deriv.

trafo

matrix: transformation of the parameter.

Value

The optimal clipping bound is computed.

Methods

clip = "numeric", L2deriv = "UnivariateDistribution", risk = "asMSE", neighbor = "ContNeighborhood": optimal clipping bound for asymtotic mean square error.
clip = "numeric", L2deriv = "UnivariateDistribution", risk = "asMSE", neighbor = "TotalVarNeighborhood": optimal clipping bound for asymtotic mean square error.
clip = "numeric", L2deriv = "EuclRandVariable", risk = "asMSE", neighbor = "ContNeighborhood": optimal clipping bound for asymtotic mean square error.
clip = "numeric", L2deriv = "UnivariateDistribution", risk = "asUnOvShoot", neighbor = "UncondNeighborhood": optimal clipping bound for asymtotic under-/overshoot risk.

References

Rieder, H. (1980) Estimates derived from robust tests. Ann. Stats. 8: 106--115.

Rieder, H. (1994) Robust Asymptotic Statistics. New York: Springer.