rgsOptIC.ALs: Computation of the optimally robust IC for ALs estimators
In RobRex: Optimally Robust Influence Curves for Regression and Scale

Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/rgsOptIC_ALs.R

The function rgsOptIC.ALs computes the optimally robust IC for ALs estimators in case of linear regression with unknown scale and (convex) contamination neighborhoods where the regressor is random; confer Subsection 7.3.1 of Kohl (2005).

1 2	rgsOptIC.ALs(r, K, A.rg.start, b.rg.Up = 1000, delta = 1e-06, itmax = 50, check = FALSE)

`r`	non-negative real: neighborhood radius.
`K`	object of class `"Distribution"`.
`A.rg.start`	positive definite and symmetric matrix: starting value for the standardizing matrix of the regression part.
`b.rg.Up`	positive real: the upper end point of the interval to be searched for b.rg.
`delta`	the desired accuracy (convergence tolerance).
`itmax`	the maximum number of iterations.
`check`	logical. Should constraints be checked.

If A.rg.start is missing, the inverse of the second moment matrix of K is used.

Object of class "ContIC"

Matthias Kohl Matthias.Kohl@stamats.de

Rieder, H. (1994) Robust Asymptotic Statistics. New York: Springer.

Kohl, M. (2005) Numerical Contributions to the Asymptotic Theory of Robustness. Bayreuth: Dissertation.

ContIC-class

## code takes some time
## Not run: 
K <- DiscreteDistribution(1:5) # = Unif({1,2,3,4,5})
IC1 <- rgsOptIC.ALs(r = 0.1, K = K)
checkIC(IC1)
Risks(IC1)
Infos(IC1)

## End(Not run)