getInfClip {ROptEstOld}R Documentation

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 signature 'numeric,
##   UnivariateDistribution, asMSE, ContNeighborhood':
getInfClip(clip, L2deriv, risk, neighbor, cent, symm, trafo)

## S4 method for signature 'numeric,
##   UnivariateDistribution, asMSE, TotalVarNeighborhood':
getInfClip(clip, L2deriv, risk, neighbor, cent, symm, trafo)

## S4 method for signature 'numeric, EuclRandVariable,
##   asMSE, ContNeighborhood':
getInfClip(clip, L2deriv, risk, neighbor, Distr, stand, cent, trafo)

## S4 method for signature '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.

Author(s)

Matthias Kohl Matthias.Kohl@stamats.de

References

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

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

Kohl, M. (2005) Numerical Contributions to the Asymptotic Theory of Robustness. Bayreuth: Dissertation.

See Also

ContIC-class, TotalVarIC-class


[Package ROptEstOld version 0.5.2 Index]