Generic function for the computation of optimally robust ICs in case of robust models with fixed neighborhoods. This function is rarely called directly.
getFixRobIC(Distr, risk, neighbor, ...)# S4 method for Norm,fiUnOvShoot,UncondNeighborhood
getFixRobIC(Distr, risk, neighbor,
sampleSize, upper, lower, maxiter, tol, warn, Algo, cont)
The optimally robust IC is computed.
object of class "Distribution".
object of class "RiskType".
object of class "Neighborhood".
additional parameters.
integer: sample size.
upper bound for the optimal clipping bound.
lower bound for the optimal clipping bound.
the maximum number of iterations.
the desired accuracy (convergence tolerance).
logical: print warnings.
"A" or "B".
"left" or "right".
computes the optimally robust influence curve for one-dimensional normal location and finite-sample under-/overshoot risk.
Matthias Kohl Matthias.Kohl@stamats.de
Computation of the optimally robust IC in sense of Huber (1968) which is also treated in Kohl (2005). The Algorithm used to compute the exact finite sample risk is introduced and explained in Kohl (2005). It is based on FFT.
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.
Kohl, M. (2005) Numerical Contributions to the Asymptotic Theory of Robustness. Bayreuth: Dissertation.
FixRobModel-class