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

In RobRex: Optimally Robust Influence Curves for Regression and Scale

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 [e-mail]

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)

RobRex documentation built on May 2, 2019, 12:40 p.m.