getFixRobIC: Generic Function for the Computation of Optimally Robust ICs

Description Usage Arguments Details Value Methods Author(s) References See Also

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

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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 [email protected]

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.

See Also

FixRobModel-class


ROptEst documentation built on May 31, 2017, 2:50 a.m.