getRiskIC | R Documentation |
Generic function for the computation of a risk for an IC.
getRiskIC(IC, risk, neighbor, L2Fam, ...)
## S4 method for signature 'HampIC,asCov,missing,missing'
getRiskIC(IC, risk, withCheck= TRUE, ...)
## S4 method for signature 'HampIC,asCov,missing,L2ParamFamily'
getRiskIC(IC, risk, L2Fam, withCheck= TRUE, ...)
## S4 method for signature 'TotalVarIC,asCov,missing,L2ParamFamily'
getRiskIC(IC, risk, L2Fam, withCheck = TRUE, ...)
IC |
object of class |
risk |
object of class |
neighbor |
object of class |
... |
additional parameters to be passed to |
L2Fam |
object of class |
withCheck |
logical: should a call to |
To make sure that the results are valid, it is recommended
to include an additional check of the IC properties of IC
using checkIC
.
The risk of an IC is computed.
asymptotic covariance of IC
read off from corresp. Risks
slot.
asymptotic covariance of IC
under L2Fam
read off from corresp. Risks
slot.
asymptotic covariance of IC
read off from corresp. Risks
slot,
resp. if this is NULL
calculates it via getInfV
.
This generic function is still under construction.
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 Risk of M-estimators on Neighborhoods.
getRiskIC
, InfRobModel-class
B <- BinomFamily(size = 25, prob = 0.25)
## classical optimal IC
IC0 <- optIC(model = B, risk = asCov())
getRiskIC(IC0, asCov())
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