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

Description

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

Usage

getInfClipRegTS(clip, ErrorL2deriv, Regressor, risk, neighbor, ...)
# S4 method for numeric,UnivariateDistribution,Distribution,asMSE,Neighborhood
getInfClipRegTS(
             clip, ErrorL2deriv, Regressor, risk, neighbor, z.comp, stand, cent)
# S4 method for numeric,UnivariateDistribution,Distribution,asMSE,Av1CondTotalVarNeighborhood
getInfClipRegTS(
             clip, ErrorL2deriv, Regressor, risk, neighbor, z.comp, stand, cent)
# S4 method for numeric,EuclRandVariable,Distribution,asMSE,Neighborhood
getInfClipRegTS(
             clip, ErrorL2deriv, Regressor, risk, neighbor, ErrorDistr, stand,
             cent, trafo)
# S4 method for numeric,UnivariateDistribution,UnivariateDistribution,asUnOvShoot,UncondNeighborhood
getInfClipRegTS(
             clip, ErrorL2deriv, Regressor, risk, neighbor, z.comp, cent)
# S4 method for numeric,UnivariateDistribution,numeric,asUnOvShoot,CondNeighborhood
getInfClipRegTS(
             clip, ErrorL2deriv, Regressor, risk, neighbor)

Arguments

clip

optimal clipping bound.

ErrorL2deriv

L2-derivative of ErrorDistr.

Regressor

regressor.

risk

object of class "RiskType".

neighbor

object of class "Neighborhood".

…

additional parameters.

cent

optimal centering constant/function.

stand

standardizing matrix.

z.comp

which components of the centering constant/function have to be computed.

ErrorDistr

error distribution.

trafo

matrix: transformation of the parameter.

Value

The optimal clipping bound/function is computed.

Methods

clip = "numeric", ErrorL2deriv = "UnivariateDistribution", Regressor = "Distribution", risk = "asMSE", neighbor = "Neighborhood": optimal clipping bound for asymtotic mean square error.
clip = "numeric", ErrorL2deriv = "UnivariateDistribution", Regressor = "Distribution", risk = "asMSE", neighbor = "Av1CondTotalVarNeighborhood": optimal clipping bound for asymtotic mean square error.
clip = "numeric", ErrorL2deriv = "EuclRandVariable", Regressor = "Distribution", risk = "asMSE", neighbor = "Neighborhood": optimal clipping bound for asymtotic mean square error.
clip = "numeric", ErrorL2deriv = "UnivariateDistribution", Regressor = "UnivariateDistribution", risk = "asUnOvShoot", neighbor = "UncondNeighborhood": optimal clipping bound for asymtotic under-/overshoot risk.
clip = "numeric", ErrorL2deriv = "UnivariateDistribution", Regressor = "numeric", risk = "asUnOvShoot", neighbor = "CondNeighborhood": optimal clipping function 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.

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