Description Usage Arguments Value Methods Author(s) References See Also
Generic function for the computation of optimally robust regression-type ICs in case of fixed robust models. This function is rarely called directly.
1 2 3 4 5 6 7 8 9 10 11 | getFixRobRegTypeIC(ErrorDistr, Regressor, risk, neighbor, ...)
## S4 method for signature
## 'Norm,UnivariateDistribution,fiUnOvShoot,UncondNeighborhood'
getFixRobRegTypeIC(ErrorDistr,
Regressor, risk, neighbor, sampleSize, upper, maxiter, tol, warn, Algo, cont)
## S4 method for signature
## 'Norm,UnivariateDistribution,fiUnOvShoot,CondNeighborhood'
getFixRobRegTypeIC(ErrorDistr,
Regressor, risk, neighbor, sampleSize, upper, maxiter, tol, warn, Algo, cont)
|
ErrorDistr |
error distribution |
Regressor |
regressor |
risk |
object of class |
neighbor |
object of class |
... |
additional parameters. |
sampleSize |
integer: sample size. |
upper |
upper bound for the optimal clipping bound. |
maxiter |
the maximum number of iterations. |
tol |
the desired accuracy (convergence tolerance). |
warn |
logical: print warnings. |
Algo |
"A" or "B". |
cont |
"left" or "right". |
The optimally robust IC is computed.
computes the optimally robust influence curve for one-dimensional normal regression and finite-sample under-/overshoot risk.
computes the optimally robust influence curve for one-dimensional normal regression and finite-sample under-/overshoot risk.
Matthias Kohl Matthias.Kohl@stamats.de
Huber, P.J. (1968) Robust Confidence Limits. Z. Wahrscheinlichkeitstheor. Verw. Geb. 10:269–278.
Rieder, H. (1989) A finite-sample minimax regression estimator. Statistics 20(2): 211–221.
Kohl, M. (2005) Numerical Contributions to the Asymptotic Theory of Robustness. Bayreuth: Dissertation.
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