rgsOptIC.BM: Computation of the optimally robust IC for BM estimators
In RobRex: Optimally Robust Influence Curves for Regression and Scale

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

View source: R/rgsOptIC_BM.R

The function rgsOptIC.BM computes the optimally robust IC for BM estimators in case of linear regression with unknown scale and (convex) contamination neighborhoods where the regressor is random. These estimators were proposed by Bednarski and Mueller (2001); confer also Subsection 7.3.3 of Kohl (2005).

1 2	rgsOptIC.BM(r, K, b.rg.start = 2.5, b.sc.0.x.start, delta = 1e-06, MAX = 100, itmax = 1000)

`r`	non-negative real: neighborhood radius.
`K`	object of class `"DiscreteDistribution"`
`b.rg.start`	positive real: starting value for b_rg.
`b.sc.0.x.start`	positive real: starting value for b_sc,0,x.
`delta`	the desired accuracy (convergence tolerance).
`itmax`	the maximum number of iterations.
`MAX`	if b_loc or b_sc,0 are beyond the admitted values, `MAX` is returned.

The computation of the optimally robust IC for BM estimators is based on optim where MAX is used to control the constraints on b_rg and b_sc,0,x.

Object of class "CondIC"

Matthias Kohl Matthias.Kohl@stamats.de

Bednarski, T and Mueller, C.H. (2001) Optimal bounded influence regression and scale M-estimators in the context of experimental design. Statistics, 35(4): 349–369.

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

CondIC-class

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

## End(Not run)