| 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