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

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

View source: R/rgsOptIC_MK.R

Description

The function rgsOptIC.MK computes the optimally robust IC for MK estimators in case of linear regression with unknown scale and (convex) contamination neighborhoods where the regressor is random; confer Subsubsection 7.2.2.1 of Kohl (2005).

Usage

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rgsOptIC.MK(r, K, ggLo = 0.5, ggUp = 1, a1.start = -0.25, a3.start = 0.25, 
             B.start, bUp = 1000, delta = 1e-06, itmax = 1000, check = FALSE)

Arguments

r

non-negative real: neighborhood radius.

K

object of class "Distribution".

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.start

real: starting value for Lagrange multiplier alpha_1.

a3.start

real: starting value for Lagrange multiplier alpha_3.

B.start

symmetric matrix: starting value for Lagrange multiplier B.

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 "IC"

Author(s)

Matthias Kohl Matthias.Kohl@stamats.de

References

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

See Also

IC-class

Examples

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

## End(Not run)

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

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