Generic function for the computation of a risk for an IC.
getRiskIC(IC, risk, neighbor, L2Fam, ...)# S4 method for IC,asCov,missing,missing
getRiskIC(IC, risk,
tol = .Machine$double.eps^0.25, withCheck = TRUE, ...)
# S4 method for IC,asCov,missing,L2ParamFamily
getRiskIC(IC, risk, L2Fam,
tol = .Machine$double.eps^0.25, withCheck = TRUE, ..., diagnostic = FALSE)
# S4 method for IC,trAsCov,missing,missing
getRiskIC(IC, risk,
tol = .Machine$double.eps^0.25, withCheck = TRUE, ...)
# S4 method for IC,trAsCov,missing,L2ParamFamily
getRiskIC(IC, risk, L2Fam,
tol = .Machine$double.eps^0.25, withCheck = TRUE, ...)
# S4 method for IC,asBias,UncondNeighborhood,missing
getRiskIC(IC, risk, neighbor,
tol = .Machine$double.eps^0.25, withCheck = TRUE, ...)
# S4 method for IC,asBias,UncondNeighborhood,L2ParamFamily
getRiskIC(IC, risk, neighbor, L2Fam,
tol = .Machine$double.eps^0.25, withCheck = TRUE, ...)
# S4 method for IC,asMSE,UncondNeighborhood,missing
getRiskIC(IC, risk, neighbor,
tol = .Machine$double.eps^0.25, withCheck = TRUE, ...)
# S4 method for IC,asMSE,UncondNeighborhood,L2ParamFamily
getRiskIC(IC, risk, neighbor, L2Fam,
tol = .Machine$double.eps^0.25, withCheck = TRUE, ...)
# S4 method for TotalVarIC,asUnOvShoot,UncondNeighborhood,missing
getRiskIC(IC, risk, neighbor)
# S4 method for IC,fiUnOvShoot,ContNeighborhood,missing
getRiskIC(IC, risk, neighbor, sampleSize, Algo = "A", cont = "left")
# S4 method for IC,fiUnOvShoot,TotalVarNeighborhood,missing
getRiskIC(IC, risk, neighbor, sampleSize, Algo = "A", cont = "left")
The risk of an IC is computed.
object of class "InfluenceCurve"
object of class "RiskType"
.
object of class "Neighborhood"
.
object of class "L2ParamFamily"
.
additional parameters (e.g. to be passed to E
).
the desired accuracy (convergence tolerance).
integer: sample size.
"A" or "B".
"left" or "right".
logical: should a call to checkIC
be done to
check accuracy (defaults to TRUE
).
logical; if TRUE
, the return value obtains
an attribute "diagnostic"
with diagnostic information on the
integration.
asymptotic covariance of IC
.
asymptotic covariance of IC
under L2Fam
.
asymptotic covariance of IC
.
asymptotic covariance of IC
under L2Fam
.
asymptotic bias of IC
under convex contaminations; uses method getBiasIC
.
asymptotic bias of IC
under convex contaminations and L2Fam
; uses method getBiasIC
.
asymptotic bias of IC
in case of total variation neighborhoods; uses method getBiasIC
.
asymptotic bias of IC
under L2Fam
in case of total variation
neighborhoods; uses method getBiasIC
.
asymptotic mean square error of IC
.
asymptotic mean square error of IC
under L2Fam
.
asymptotic under-/overshoot risk of IC
.
finite-sample under-/overshoot risk of IC
.
finite-sample under-/overshoot risk of IC
.
Matthias Kohl Matthias.Kohl@stamats.de
Peter Ruckdeschel peter.ruckdeschel@uni-oldenburg.de
To make sure that the results are valid, it is recommended
to include an additional check of the IC properties of IC
using checkIC
.
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