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