RobAStBase: getBiasIC – R documentation

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RobAStBase

getBiasIC

Generic function for the computation of the asymptotic bias for an IC

Description

Generic function for the computation of the asymptotic bias for an IC.

Usage

getBiasIC(IC, neighbor, ...)

## S4 method for signature 'IC,UncondNeighborhood'
getBiasIC(IC, neighbor, L2Fam,
             biastype = symmetricBias(), normtype = NormType(),
             tol = .Machine$double.eps^0.25, numbeval = 1e5, withCheck = TRUE, ...)

Arguments

`IC`	object of class `"InfluenceCurve"`
`neighbor`	object of class `"Neighborhood"`.
`L2Fam`	object of class `"L2ParamFamily"`.
`biastype`	object of class `"BiasType"`
`normtype`	object of class `"NormType"`
`tol`	the desired accuracy (convergence tolerance).
`numbeval`	number of evalation points.
`withCheck`	logical: should a call to `checkIC` be done to check accuracy (defaults to `TRUE`).
`...`	additional parameters to be passed to expectation `E`

Value

The bias of the IC is computed.

Methods

IC = "IC", neighbor = "UncondNeighborhood": determines the as. bias by random evaluation of the IC; this random evaluation is done by the internal S4-method .evalBiasIC; this latter dispatches according to the signature IC, neighbor, biastype.
For signature IC="IC", neighbor = "ContNeighborhood", biastype = "BiasType", also an argument normtype is used to be able to use self- or information standardizing norms; besides this the signatures IC="IC", neighbor = "TotalVarNeighborhood", biastype = "BiasType", IC="IC", neighbor = "ContNeighborhood", biastype = "onesidedBias", and IC="IC", neighbor = "ContNeighborhood", biastype = "asymmetricBias" are implemented.

Note

This generic function is still under construction.

Author(s)

Peter Ruckdeschel peter.ruckdeschel@uni-oldenburg.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.

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

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

Ruckdeschel, P. and Kohl, M. (2005) Computation of the Finite Sample Bias of M-estimators on Neighborhoods.