getRiskIC {ROptEstOld} | R Documentation |
Generic function for the computation of a risk for an IC.
getRiskIC(IC, risk, neighbor, L2Fam, ...) ## S4 method for signature 'IC, asCov, missing, missing': getRiskIC(IC, risk, tol = .Machine$double.eps^0.25) ## S4 method for signature 'IC, asCov, missing, ## L2ParamFamily': getRiskIC(IC, risk, L2Fam, tol = .Machine$double.eps^0.25) ## S4 method for signature 'IC, trAsCov, missing, missing': getRiskIC(IC, risk, tol = .Machine$double.eps^0.25) ## S4 method for signature 'IC, trAsCov, missing, ## L2ParamFamily': getRiskIC(IC, risk, L2Fam, tol = .Machine$double.eps^0.25) ## S4 method for signature 'IC, asBias, ContNeighborhood, ## missing': getRiskIC(IC, risk, neighbor, tol = .Machine$double.eps^0.25) ## S4 method for signature 'IC, asBias, ContNeighborhood, ## L2ParamFamily': getRiskIC(IC, risk, neighbor, L2Fam, tol = .Machine$double.eps^0.25) ## S4 method for signature 'IC, asBias, ## TotalVarNeighborhood, missing': getRiskIC(IC, risk, neighbor, tol = .Machine$double.eps^0.25) ## S4 method for signature 'IC, asBias, ## TotalVarNeighborhood, L2ParamFamily': getRiskIC(IC, risk, neighbor, L2Fam, tol = .Machine$double.eps^0.25) ## S4 method for signature 'IC, asMSE, UncondNeighborhood, ## missing': getRiskIC(IC, risk, neighbor, tol = .Machine$double.eps^0.25) ## S4 method for signature 'IC, asMSE, UncondNeighborhood, ## L2ParamFamily': getRiskIC(IC, risk, neighbor, L2Fam, tol = .Machine$double.eps^0.25) ## S4 method for signature 'TotalVarIC, asUnOvShoot, ## UncondNeighborhood, missing': getRiskIC(IC, risk, neighbor) ## S4 method for signature 'IC, fiUnOvShoot, ## ContNeighborhood, missing': getRiskIC(IC, risk, neighbor, sampleSize, Algo = "A", cont = "left") ## S4 method for signature 'IC, fiUnOvShoot, ## TotalVarNeighborhood, missing': getRiskIC(IC, risk, neighbor, sampleSize, Algo = "A", cont = "left")
IC |
object of class "InfluenceCurve" |
risk |
object of class "RiskType" . |
neighbor |
object of class "Neighborhood" . |
L2Fam |
object of class "L2ParamFamily" . |
... |
additional parameters |
tol |
the desired accuracy (convergence tolerance). |
sampleSize |
integer: sample size. |
Algo |
"A" or "B". |
cont |
"left" or "right". |
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.
IC
. IC
under L2Fam
. IC
. IC
under L2Fam
. IC
under convex contaminations. IC
under convex contaminations and L2Fam
. IC
in case of total variation neighborhoods. IC
under L2Fam
in case of total variation
neighborhoods. IC
. IC
under L2Fam
. IC
. IC
. IC
. This generic function is still under construction.
Matthias Kohl Matthias.Kohl@stamats.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-methods
, InfRobModel-class