Generic function for the computation of the asymptotic bias for an IC.
getBiasIC(IC, neighbor, ...)# S4 method for IC,UncondNeighborhood
getBiasIC(IC, neighbor, L2Fam,
biastype = symmetricBias(), normtype = NormType(),
tol = .Machine$double.eps^0.25, numbeval = 1e5, withCheck = TRUE, ...)
The bias of the IC is computed.
object of class "InfluenceCurve"
object of class "Neighborhood".
object of class "L2ParamFamily".
object of class "BiasType"
object of class "NormType"
the desired accuracy (convergence tolerance).
number of evalation points.
logical: should a call to checkIC be done to
check accuracy (defaults to TRUE).
additional parameters to be passed to expectation E
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.
Peter Ruckdeschel peter.ruckdeschel@uni-oldenburg.de
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.
getRiskIC-methods, InfRobModel-class