getInfRobIC {ROptEstOld} | 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) ## S4 method for signature 'UnivariateDistribution,asCov,TotalVarNeighborhood' getInfRobIC(L2deriv, risk, neighbor, Finfo, trafo) ## S4 method for signature 'RealRandVariable,asCov,ContNeighborhood' getInfRobIC(L2deriv, risk, neighbor, Distr, Finfo, trafo) ## S4 method for signature 'UnivariateDistribution,asBias,ContNeighborhood' getInfRobIC(L2deriv, risk, neighbor, symm, Finfo, trafo, upper, maxiter, tol, warn) ## S4 method for signature 'UnivariateDistribution,asBias,TotalVarNeighborhood' getInfRobIC(L2deriv, risk, neighbor, symm, Finfo, trafo, upper, maxiter, tol, warn) ## S4 method for signature 'RealRandVariable,asBias,ContNeighborhood' getInfRobIC(L2deriv, risk, neighbor, Distr, DistrSymm, L2derivSymm, L2derivDistrSymm, Finfo, z.start, A.start, trafo, upper, maxiter, tol, warn) ## S4 method for signature 'UnivariateDistribution,asHampel,UncondNeighborhood' getInfRobIC(L2deriv, risk, neighbor, symm, Finfo, trafo, upper, maxiter, tol, warn) ## S4 method for signature 'RealRandVariable,asHampel,ContNeighborhood' getInfRobIC(L2deriv, risk, neighbor, Distr, DistrSymm, L2derivSymm, L2derivDistrSymm, Finfo, trafo, z.start, A.start, upper, maxiter, tol, warn) ## S4 method for signature 'UnivariateDistribution,asGRisk,UncondNeighborhood' getInfRobIC(L2deriv, risk, neighbor, symm, Finfo, trafo, upper, maxiter, tol, warn) ## S4 method for signature 'RealRandVariable,asGRisk,ContNeighborhood' getInfRobIC(L2deriv, risk, neighbor, Distr, DistrSymm, L2derivSymm, L2derivDistrSymm, Finfo, trafo, z.start, A.start, upper, maxiter, tol, warn) ## S4 method for signature ## 'UnivariateDistribution,asUnOvShoot,UncondNeighborhood' getInfRobIC(L2deriv, risk, neighbor, symm, Finfo, trafo, upper, maxiter, tol, warn)
L2deriv |
L2-derivative of some L2-differentiable family of probability measures. |
risk |
object of class |
neighbor |
object of class |
... |
additional parameters. |
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. |
maxiter |
the maximum number of iterations. |
tol |
the desired accuracy (convergence tolerance). |
warn |
logical: print warnings. |
The optimally robust IC is computed.
computes the classical optimal influence curve for L2 differentiable parametric families with unknown one-dimensional parameter.
computes the classical optimal influence curve for L2 differentiable parametric families with unknown one-dimensional 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.
computes the bias optimal influence curve for L2 differentiable parametric families with unknown one-dimensional parameter.
computes the bias optimal influence curve for L2 differentiable parametric families with unknown one-dimensional 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 one-dimensional 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.
computes the optimally robust influence curve for L2 differentiable parametric families with unknown one-dimensional 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.
computes the optimally robust influence curve for one-dimensional L2 differentiable parametric families and asymptotic under-/overshoot risk.
Matthias Kohl Matthias.Kohl@stamats.de
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.