getRiskIC {ROptEst} | R Documentation |
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 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, ...)
Arguments
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 |
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
.
Value
The risk of an IC is computed.
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
underL2Fam
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 isNULL
calculates it viagetInfV
.
Note
This generic function is still under construction.
Author(s)
Peter Ruckdeschel peter.ruckdeschel@uni-oldenburg.de
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
Examples
B <- BinomFamily(size = 25, prob = 0.25)
## classical optimal IC
IC0 <- optIC(model = B, risk = asCov())
getRiskIC(IC0, asCov())