| getBiasIC {RobAStBase} | R Documentation |
Generic function for the computation of the asymptotic bias for an IC.
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)
IC |
object of class "InfluenceCurve" |
neighbor |
object of class "Neighborhood". |
... |
additional parameters |
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. |
The bias of the IC is computed.
.evalBiasIC; this latter dispatches according to
the signature IC, neighbor, biastype.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.
This generic function is still under construction.
Peter Ruckdeschel Peter.Ruckdeschel@itwm.fraunhofer.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