ROptEst: getFixRobIC – R documentation

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getFixRobIC

Generic Function for the Computation of Optimally Robust ICs

Description

Generic function for the computation of optimally robust ICs in case of robust models with fixed neighborhoods. This function is rarely called directly.

Usage

getFixRobIC(Distr, risk, neighbor, ...)

## S4 method for signature 'Norm,fiUnOvShoot,UncondNeighborhood'
getFixRobIC(Distr, risk, neighbor, 
          sampleSize, upper, lower, maxiter, tol, warn, Algo, cont)

Arguments

`Distr`	object of class `"Distribution"`.
`risk`	object of class `"RiskType"`.
`neighbor`	object of class `"Neighborhood"`.
`...`	additional parameters.
`sampleSize`	integer: sample size.
`upper`	upper bound for the optimal clipping bound.
`lower`	lower bound for the optimal clipping bound.
`maxiter`	the maximum number of iterations.
`tol`	the desired accuracy (convergence tolerance).
`warn`	logical: print warnings.
`Algo`	"A" or "B".
`cont`	"left" or "right".

Details

Computation of the optimally robust IC in sense of Huber (1968) which is also treated in Kohl (2005). The Algorithm used to compute the exact finite sample risk is introduced and explained in Kohl (2005). It is based on FFT.

Value

The optimally robust IC is computed.

Methods

Distr = "Norm", risk = "fiUnOvShoot", neighbor = "UncondNeighborhood": computes the optimally robust influence curve for one-dimensional normal location and finite-sample under-/overshoot risk.

Author(s)

Matthias Kohl Matthias.Kohl@stamats.de

References

Huber, P.J. (1968) Robust Confidence Limits. Z. Wahrscheinlichkeitstheor. Verw. Geb. 10:269–278.

Rieder, H. (1980) Estimates derived from robust tests. Ann. Stats. 8: 106-115.

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