rgsOptIC.ALs: Computation of the optimally robust IC for ALs estimators

In RobRex: Optimally Robust Influence Curves for Regression and Scale

Computation of the optimally robust IC for ALs estimators

Description

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).

Usage

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

Arguments

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.

Details

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

Value

Object of class "ContIC"

Author(s)

Matthias Kohl [e-mail]

References

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

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

See Also

ContIC-class

Examples

## 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)

RobRex documentation built on Jan. 29, 2024, 3:01 a.m.