RobRex (version 1.2.0)

rgsOptIC.ALs: 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.

Value

Object of class "ContIC"

Details

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

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

# NOT RUN {
## 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)
# }