getInfClipRegTS {ROptRegTS}R Documentation

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 signature 'numeric,
##   UnivariateDistribution, Distribution, asMSE,
##   Neighborhood':
getInfClipRegTS(clip, 
                ErrorL2deriv, Regressor, risk, neighbor, z.comp, stand, cent)

## S4 method for signature 'numeric,
##   UnivariateDistribution, Distribution, asMSE,
##   Av1CondTotalVarNeighborhood':
getInfClipRegTS(clip, 
                ErrorL2deriv, Regressor, risk, neighbor, z.comp, stand, cent)

## S4 method for signature 'numeric, EuclRandVariable,
##   Distribution, asMSE, Neighborhood':
getInfClipRegTS(clip, ErrorL2deriv, 
                Regressor, risk, neighbor, ErrorDistr, stand, cent, trafo)

## S4 method for signature 'numeric,
##   UnivariateDistribution, UnivariateDistribution,
##   asUnOvShoot, UncondNeighborhood':
getInfClipRegTS(clip, 
                ErrorL2deriv, Regressor, risk, neighbor, z.comp, cent)

## S4 method for signature '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.

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, Av1CondContIC-class, Av2CondContIC-class, Av1CondTotalVarIC-class, CondContIC-class, CondTotalVarIC-class


[Package ROptRegTS version 0.6.1 Index]