rgsOptIC.Mc {RobRex}R Documentation

Computation of the optimally robust IC for Mc estimators

Description

The function rgsOptIC.Mc computes the optimally robust conditionally centered IC for Mc estimators in case of linear regression with unknown scale and average conditional (convex) contamination neighborhoods where the regressor is random; confer Subsubsection 7.2.2.2 of Kohl (2005).

Usage

rgsOptIC.Mc(r, K, ggLo = 0.5, ggUp = 1, a1.x.start, a3.start = 0.25, 
             bUp = 1000, delta = 1e-05, itmax = 1000, check = FALSE)

Arguments

r non-negative real: neighborhood radius.
K object of class "DiscreteDistribution"
ggLo positive real: the lower end point of the interval to be searched for gamma.
ggUp positive real: the upper end point of the interval to be searched for gamma.
a1.x.start real: starting value for the Lagrange multiplier function alpha_1(x).
a3.start real: starting value for Lagrange multiplier alpha_3.
bUp positive real: the upper end point of the interval to be searched for b.
delta the desired accuracy (convergence tolerance).
itmax the maximum number of iterations.
check logical. Should constraints be checked.

Value

Object of class "CondIC"

Author(s)

Matthias Kohl Matthias.Kohl@stamats.de

References

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

See Also

CondIC-class

Examples

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

[Package RobRex version 0.6.1 Index]