ROptEst (version 1.3.4)

getRiskIC: Generic function for the computation of a risk for an IC

Description

Generic function for the computation of a risk for an IC.

Usage

getRiskIC(IC, risk, neighbor, L2Fam, ...)
# S4 method for HampIC,asCov,missing,missing
getRiskIC(IC, risk, withCheck= TRUE, ...)
# S4 method for HampIC,asCov,missing,L2ParamFamily
getRiskIC(IC, risk, L2Fam, withCheck= TRUE, ...)
# S4 method for TotalVarIC,asCov,missing,L2ParamFamily
getRiskIC(IC, risk, L2Fam, withCheck = TRUE, ...)

Value

The risk of an IC is computed.

Arguments

IC

object of class "InfluenceCurve"

risk

object of class "RiskType".

neighbor

object of class "Neighborhood"; missing in the methods described here.

...

additional parameters to be passed to E

L2Fam

object of class "L2ParamFamily".

withCheck

logical: should a call to checkIC be done to check accuracy (defaults to TRUE; ignored if nothing is computed but simply a slot is read out).

Methods

IC = "HampIC", risk = "asCov", neighbor = "missing", L2Fam = "missing"

asymptotic covariance of IC read off from corresp. Risks slot.

IC = "HampIC", risk = "asCov", neighbor = "missing", L2Fam = "L2ParamFamily"

asymptotic covariance of IC under L2Fam read off from corresp. Risks slot.

IC = "TotalVarIC", risk = "asCov", neighbor = "missing", L2Fam = "L2ParamFamily"

asymptotic covariance of IC read off from corresp. Risks slot, resp. if this is NULL calculates it via getInfV.

Author

Peter Ruckdeschel

Details

To make sure that the results are valid, it is recommended to include an additional check of the IC properties of IC using checkIC.

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 Risk of M-estimators on Neighborhoods.

See Also

getRiskIC, InfRobModel-class

Examples

B <- BinomFamily(size = 25, prob = 0.25)

## classical optimal IC
IC0 <- optIC(model = B, risk = asCov())
getRiskIC(IC0, asCov())