# getInfStand: Generic Function for the Computation of the Standardizing... In ROptEst: Optimally Robust Estimation

 getInfStand R Documentation

## Generic Function for the Computation of the Standardizing Matrix

### Description

Generic function for the computation of the standardizing matrix which takes care of the Fisher consistency of the corresponding IC. This function is rarely called directly. It is used to compute optimally robust ICs.

### Usage

```getInfStand(L2deriv, neighbor, biastype, ...)

## S4 method for signature 'UnivariateDistribution,ContNeighborhood,BiasType'
getInfStand(L2deriv,
neighbor, biastype, clip, cent, trafo)

## S4 method for signature
## 'UnivariateDistribution,TotalVarNeighborhood,BiasType'
getInfStand(L2deriv,
neighbor, biastype, clip, cent, trafo)

## S4 method for signature 'RealRandVariable,UncondNeighborhood,BiasType'
getInfStand(L2deriv,
neighbor, biastype, Distr, A.comp, cent, trafo, w, ...)

## S4 method for signature
## 'UnivariateDistribution,ContNeighborhood,onesidedBias'
getInfStand(L2deriv,
neighbor, biastype, clip, cent, trafo, ...)

## S4 method for signature
## 'UnivariateDistribution,ContNeighborhood,asymmetricBias'
getInfStand(L2deriv,
neighbor, biastype, clip, cent, trafo)
```

### Arguments

 `L2deriv` L2-derivative of some L2-differentiable family of probability measures. `neighbor` object of class `"Neighborhood"`. `biastype` object of class `"BiasType"`. `...` additional parameters, in particular for expectation `E`. `clip` optimal clipping bound. `cent` optimal centering constant. `Distr` object of class `"Distribution"`. `trafo` matrix: transformation of the parameter. `A.comp` matrix: indication which components of the standardizing matrix have to be computed. `w` object of class `RobWeight`; current weight.

### Value

The standardizing matrix is computed.

### Methods

L2deriv = "UnivariateDistribution", neighbor = "ContNeighborhood", biastype = "BiasType"

computes standardizing matrix for symmetric bias.

L2deriv = "UnivariateDistribution", neighbor = "TotalVarNeighborhood", biastype = "BiasType"

computes standardizing matrix for symmetric bias.

L2deriv = "RealRandVariable", neighbor = "UncondNeighborhood", biastype = "BiasType"

computes standardizing matrix for symmetric bias.

L2deriv = "UnivariateDistribution", neighbor = "ContNeighborhood", biastype = "onesidedBias"

computes standardizing matrix for onesided bias.

L2deriv = "UnivariateDistribution", neighbor = "ContNeighborhood", biastype = "asymmetricBias"

computes standardizing matrix for asymmetric bias.

### Author(s)

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

### References

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

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`