Description Usage Arguments Details Value Methods Author(s) References See Also
Generic function for the computation of optimally robust ICs in case of robust models with fixed neighborhoods. This function is rarely called directly.
1 2 3 4 5  getFixRobIC(Distr, risk, neighbor, ...)
## S4 method for signature 'Norm,fiUnOvShoot,UncondNeighborhood'
getFixRobIC(Distr, risk, neighbor,
sampleSize, upper, lower, maxiter, tol, warn, Algo, cont)

Distr 
object of class 
risk 
object of class 
neighbor 
object of class 
... 
additional parameters. 
sampleSize 
integer: sample size. 
upper 
upper bound for the optimal clipping bound. 
lower 
lower 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". 
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.
The optimally robust IC is computed.
computes the optimally robust influence curve for onedimensional normal location and finitesample under/overshoot risk.
Matthias Kohl [email protected]
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: 106115.
Kohl, M. (2005) Numerical Contributions to the Asymptotic Theory of Robustness. Bayreuth: Dissertation.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.