# getInfGamma: Generic Function for the Computation of the Optimal Clipping... In ROptEstOld: Optimally Robust Estimation - Old Version

## Description

Generic function for the computation of the optimal clipping bound. This function is rarely called directly. It is called by `getInfClip` to compute optimally robust ICs.

## Usage

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15``` ```getInfGamma(L2deriv, risk, neighbor, ...) ## S4 method for signature 'UnivariateDistribution,asMSE,ContNeighborhood' getInfGamma(L2deriv, risk, neighbor, cent, clip) ## S4 method for signature ## 'UnivariateDistribution,asGRisk,TotalVarNeighborhood' getInfGamma(L2deriv, risk, neighbor, cent, clip) ## S4 method for signature 'RealRandVariable,asMSE,ContNeighborhood' getInfGamma(L2deriv, risk, neighbor, Distr, stand, cent, clip) ## S4 method for signature ## 'UnivariateDistribution,asUnOvShoot,ContNeighborhood' getInfGamma(L2deriv, risk, neighbor, cent, clip) ```

## Arguments

 `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. `clip` optimal clipping bound. `stand` standardizing matrix. `Distr` object of class `"Distribution"`.

## Details

The function is used in case of asymptotic G-risks; confer Ruckdeschel and Rieder (2004).

## Methods

L2deriv = "UnivariateDistribution", risk = "asMSE", neighbor = "ContNeighborhood"

used by `getInfClip`.

L2deriv = "UnivariateDistribution", risk = "asGRisk", neighbor = "TotalVarNeighborhood"

used by `getInfClip`.

L2deriv = "RealRandVariable", risk = "asMSE", neighbor = "ContNeighborhood"

used by `getInfClip`.

L2deriv = "UnivariateDistribution", risk = "asUnOvShoot", neighbor = "ContNeighborhood"

used by `getInfClip`.

## 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.

Ruckdeschel, P. and Rieder, H. (2004) Optimal Influence Curves for General Loss Functions. Statistics & Decisions (submitted).

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

`asGRisk-class`, `asMSE-class`, `asUnOvShoot-class`, `ContIC-class`, `TotalVarIC-class`