# getInfClip: Generic Function for the Computation of the Optimal Clipping... In ROptEst: Optimally Robust Estimation

 getInfClip 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, biastype, cent, symm, trafo)

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

## S4 method for signature
## 'numeric,UnivariateDistribution,asL1,ContNeighborhood'
getInfClip(
clip, L2deriv, risk, neighbor, biastype, cent, symm, trafo)

## S4 method for signature
## 'numeric,UnivariateDistribution,asL1,TotalVarNeighborhood'
getInfClip(
clip, L2deriv, risk, neighbor, biastype, cent, symm, trafo)

## S4 method for signature
## 'numeric,UnivariateDistribution,asL4,ContNeighborhood'
getInfClip(
clip, L2deriv, risk, neighbor, biastype, cent, symm, trafo)

## S4 method for signature
## 'numeric,UnivariateDistribution,asL4,TotalVarNeighborhood'
getInfClip(
clip, L2deriv, risk, neighbor, biastype, cent, symm, trafo)

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

## S4 method for signature
## 'numeric,UnivariateDistribution,asUnOvShoot,UncondNeighborhood'
getInfClip(
clip, L2deriv, risk, neighbor, biastype, cent, symm, trafo)

## S4 method for signature
## 'numeric,UnivariateDistribution,asSemivar,ContNeighborhood'
getInfClip(
clip, L2deriv, risk, neighbor, biastype, 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, in particular for expectation `E` `biastype` object of class `"BiasType"` `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 = "UncondNeighborhood"

optimal clipping bound for asymtotic mean square error.

clip = "numeric", L2deriv = "UnivariateDistribution", risk = "asL1", neighbor = "ContNeighborhood"

optimal clipping bound for asymtotic mean absolute error.

clip = "numeric", L2deriv = "UnivariateDistribution", risk = "asL1", neighbor = "TotalVarNeighborhood"

optimal clipping bound for asymtotic mean absolute error.

clip = "numeric", L2deriv = "UnivariateDistribution", risk = "asL4", neighbor = "ContNeighborhood"

optimal clipping bound for asymtotic mean power 4 error.

clip = "numeric", L2deriv = "UnivariateDistribution", risk = "asL4", neighbor = "TotalVarNeighborhood"

optimal clipping bound for asymtotic mean power 4 error.

clip = "numeric", L2deriv = "UnivariateDistribution", risk = "asUnOvShoot", neighbor = "UncondNeighborhood"

optimal clipping bound for asymtotic under-/overshoot risk.

clip = "numeric", L2deriv = "UnivariateDistribution", risk = "asSemivar", neighbor = "ContNeighborhood"

optimal clipping bound for asymtotic semivariance.

### Author(s)

Matthias Kohl Matthias.Kohl@stamats.de, Peter Ruckdeschel peter.ruckdeschel@uni-oldenburg.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 22, 201-223.

Ruckdeschel, P. (2005) Optimally One-Sided Bounded Influence Curves. Mathematical Methods in Statistics 14(1), 105-131.

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

`ContIC-class`, `TotalVarIC-class`