rlsOptIC.AL {RobLox}R Documentation

Computation of the optimally robust IC for AL estimators

Description

The function rlsOptIC.AL computes the optimally robust IC for AL estimators in case of normal location with unknown scale and (convex) contamination neighborhoods. The definition of these estimators can be found in Section 8.2 of Kohl (2005).

Usage

rlsOptIC.AL(r, A.loc.start = 1, a.sc.start = 0, A.sc.start = 0.5, 
            bUp = 1000, delta = 1e-06, itmax = 100, check = FALSE)

Arguments

r non-negative real: neighborhood radius.
A.loc.start positive real: starting value for the standardizing constant of the location part.
a.sc.start real: starting value for centering constant of the scale part.
A.sc.start positive real: starting value for the standardizing constant of the scale part.
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.

Details

The Lagrange multipliers contained in the expression of the optimally robust IC can be accessed via the accessor functions cent, clip and stand.

Value

Object of class "ContIC"

Author(s)

Matthias Kohl robast@gmx.de

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

IC1 <- rlsOptIC.AL(r = 0.1, check = TRUE)
distrExOptions(ErelativeTolerance, 1e-12)
checkIC(IC1)
distrExOptions(ErelativeTolerance, .Machine$double.eps^0.25) # default
Risks(IC1)
cent(IC1)
clip(IC1)
stand(IC1)
plot(IC1)
x11()
infoPlot(IC1)

[Package RobLox version 0.3-9 Index]