getInfRobIC  R Documentation 
Generic function for the computation of optimally robust ICs in case of infinitesimal robust models. This function is rarely called directly.
getInfRobIC(L2deriv, risk, neighbor, ...)
## S4 method for signature 'UnivariateDistribution,asCov,ContNeighborhood'
getInfRobIC(L2deriv,
risk, neighbor, Finfo, trafo, verbose = NULL)
## S4 method for signature 'UnivariateDistribution,asCov,TotalVarNeighborhood'
getInfRobIC(L2deriv,
risk, neighbor, Finfo, trafo, verbose = NULL)
## S4 method for signature 'RealRandVariable,asCov,UncondNeighborhood'
getInfRobIC(L2deriv, risk,
neighbor, Distr, Finfo, trafo, QuadForm = diag(nrow(trafo)),
verbose = NULL)
## S4 method for signature 'UnivariateDistribution,asBias,UncondNeighborhood'
getInfRobIC(L2deriv,
risk, neighbor, symm, trafo, maxiter, tol, warn, Finfo,
verbose = NULL, ...)
## S4 method for signature 'RealRandVariable,asBias,UncondNeighborhood'
getInfRobIC(L2deriv, risk,
neighbor, Distr, DistrSymm, L2derivSymm,
L2derivDistrSymm, z.start, A.start, Finfo, trafo,
maxiter, tol, warn, verbose = NULL, ...)
## S4 method for signature 'UnivariateDistribution,asHampel,UncondNeighborhood'
getInfRobIC(L2deriv,
risk, neighbor, symm, Finfo, trafo, upper = NULL,
lower=NULL, maxiter, tol, warn, noLow = FALSE,
verbose = NULL, checkBounds = TRUE, ...)
## S4 method for signature 'RealRandVariable,asHampel,UncondNeighborhood'
getInfRobIC(L2deriv, risk,
neighbor, Distr, DistrSymm, L2derivSymm,
L2derivDistrSymm, Finfo, trafo, onesetLM = FALSE,
z.start, A.start, upper = NULL, lower=NULL,
OptOrIter = "iterate", maxiter, tol, warn,
verbose = NULL, checkBounds = TRUE, ...,
.withEvalAsVar = TRUE)
## S4 method for signature
## 'UnivariateDistribution,asAnscombe,UncondNeighborhood'
getInfRobIC(
L2deriv, risk, neighbor, symm, Finfo, trafo, upper = NULL,
lower=NULL, maxiter, tol, warn, noLow = FALSE,
verbose = NULL, checkBounds = TRUE, ...)
## S4 method for signature 'RealRandVariable,asAnscombe,UncondNeighborhood'
getInfRobIC(L2deriv,
risk, neighbor, Distr, DistrSymm, L2derivSymm,
L2derivDistrSymm, Finfo, trafo, onesetLM = FALSE,
z.start, A.start, upper = NULL, lower=NULL,
OptOrIter = "iterate", maxiter, tol, warn,
verbose = NULL, checkBounds = TRUE, ...)
## S4 method for signature 'UnivariateDistribution,asGRisk,UncondNeighborhood'
getInfRobIC(L2deriv,
risk, neighbor, symm, Finfo, trafo, upper = NULL,
lower = NULL, maxiter, tol, warn, noLow = FALSE,
verbose = NULL, ...)
## S4 method for signature 'RealRandVariable,asGRisk,UncondNeighborhood'
getInfRobIC(L2deriv, risk,
neighbor, Distr, DistrSymm, L2derivSymm,
L2derivDistrSymm, Finfo, trafo, onesetLM = FALSE, z.start,
A.start, upper = NULL, lower = NULL, OptOrIter = "iterate",
maxiter, tol, warn, verbose = NULL, withPICcheck = TRUE,
..., .withEvalAsVar = TRUE)
## S4 method for signature
## 'UnivariateDistribution,asUnOvShoot,UncondNeighborhood'
getInfRobIC(
L2deriv, risk, neighbor, symm, Finfo, trafo,
upper, lower, maxiter, tol, warn, verbose = NULL, ...)
L2deriv 
L2derivative of some L2differentiable family of probability measures. 
risk 
object of class 
neighbor 
object of class 
... 
additional parameters (mainly for 
Distr 
object of class 
symm 
logical: indicating symmetry of 
DistrSymm 
object of class 
L2derivSymm 
object of class 
L2derivDistrSymm 
object of class 
Finfo 
Fisher information matrix. 
z.start 
initial value for the centering constant. 
A.start 
initial value for the standardizing matrix. 
trafo 
matrix: transformation of the parameter. 
upper 
upper bound for the optimal clipping bound. 
lower 
lower bound for the optimal clipping bound. 
OptOrIter 
character; which method to be used for determining Lagrange
multipliers 
maxiter 
the maximum number of iterations. 
tol 
the desired accuracy (convergence tolerance). 
warn 
logical: print warnings. 
noLow 
logical: is lower case to be computed? 
onesetLM 
logical: use one set of Lagrange multipliers? 
QuadForm 
matrix of (or which may coerced to) class

verbose 
logical: if 
checkBounds 
logical: if 
withPICcheck 
logical: at the end of the algorithm, shall we check
how accurately this is a pIC; this will only be done if

.withEvalAsVar 
logical (of length 1):
if 
The optimally robust IC is computed.
computes the classical optimal influence curve for L2 differentiable parametric families with unknown onedimensional parameter.
computes the classical optimal influence curve for L2 differentiable parametric families with unknown onedimensional parameter.
computes the classical optimal influence curve for L2 differentiable
parametric families with unknown k
dimensional parameter
(k > 1
) where the underlying distribution is univariate;
for total variation neighborhoods only is implemented for the case
where there is a 1\times k
transformation trafo
matrix.
computes the bias optimal influence curve for L2 differentiable parametric families with unknown onedimensional parameter.
computes the bias optimal influence curve for L2 differentiable
parametric families with unknown k
dimensional parameter
(k > 1
) where the underlying distribution is univariate.
computes the optimally robust influence curve for L2 differentiable parametric families with unknown onedimensional parameter.
computes the optimally robust influence curve for L2 differentiable
parametric families with unknown k
dimensional parameter
(k > 1
) where the underlying distribution is univariate;
for total variation neighborhoods only is implemented for the case
where there is a 1\times k
transformation trafo
matrix.
computes the optimally biasrobust influence curve to given ARE in the ideal model for L2 differentiable parametric families with unknown onedimensional parameter.
computes the optimally biasrobust influence curve to given ARE in the
ideal modelfor L2 differentiable
parametric families with unknown k
dimensional parameter
(k > 1
) where the underlying distribution is univariate;
for total variation neighborhoods only is implemented for the case
where there is a 1\times k
transformation trafo
matrix.
computes the optimally robust influence curve for L2 differentiable parametric families with unknown onedimensional parameter.
computes the optimally robust influence curve for L2 differentiable
parametric families with unknown k
dimensional parameter
(k > 1
) where the underlying distribution is univariate;
for total variation neighborhoods only is implemented for the case
where there is a 1\times k
transformation trafo
matrix.
computes the optimally robust influence curve for onedimensional L2 differentiable parametric families and asymptotic under/overshoot risk.
Matthias Kohl Matthias.Kohl@stamats.de,
Peter Ruckdeschel peter.ruckdeschel@unioldenburg.de
Rieder, H. (1980) Estimates derived from robust tests. Ann. Stats. 8: 106115.
Rieder, H. (1994) Robust Asymptotic Statistics. New York: Springer.
Ruckdeschel, P. and Rieder, H. (2004) Optimal Influence Curves for General Loss Functions. Statistics & Decisions 22: 201223.
Ruckdeschel, P. (2005) Optimally OneSided Bounded Influence Curves. Mathematical Methods in Statistics 14(1), 105131.
Kohl, M. (2005) Numerical Contributions to the Asymptotic Theory of Robustness. Bayreuth: Dissertation.
InfRobModelclass
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