CondTotalVarIC: Generating function for CondTotalVarIC-class

Description

Generates an object of class "CondTotalVarIC"; i.e., an influence curves $\eta$ of the form $$\eta = max(c(x), min(A x\Lambda_f, b(x)))$$ with lower clipping function $c$, upper clipping function $b$ and standardizing matrix $A$. $\Lambda_f$ stands for the L2 derivative of the corresponding error distribution.

Usage

CondTotalVarIC(name, CallL2Fam = call("L2RegTypeFamily"), 
        Curve = EuclRandVarList(RealRandVariable(
                Map = list(function(x) {x[1] * x[2]}),
                Domain = EuclideanSpace(dimension = 2))),
        Risks, Infos, 
        clipUp = RealRandVariable(Map = list(function(x) {Inf}), Domain = Reals()), 
        stand = as.matrix(1), 
        clipLo = RealRandVariable(Map = list(function(x) {-Inf}), Domain = Reals()), 
        lowerCase = NULL, neighborRadius = 0, neighborRadiusCurve = function(x){1})

Arguments

name

object of class "character".

CallL2Fam

object of class "call": creates an object of the underlying L2-differentiable regression type family.

Curve

object of class "EuclRandVarList"

Risks

object of class "list": list of risks; cf. RiskType-class.

Infos

matrix of characters with two columns named method and message: additional informations.

clipUp

object of class "RealRandVariable": upper clipping function.

clipLo

object of class "RealRandVariable": lower clipping function.

stand

matrix: standardizing matrix.

lowerCase

optional constant for lower case solution.

neighborRadius

radius of the corresponding conditional total variation neighborhood.

neighborRadiusCurve

radius curve of the corresponding conditional total variation neighborhood.

Value

Object of class "CondTotalVarIC"

References

Rieder, H. (1994) Robust Asymptotic Statistics. New York: Springer.

Kohl, M. (2005) Numerical Contributions to the Asymptotic Theory of Robustness. Bayreuth: Dissertation.

Examples

Run this code

# NOT RUN {
IC1 <- CondTotalVarIC()
IC1
# }