rsOptIC: Computation of the optimally robust IC for AL estimators
In RobLox: Optimally Robust Influence Curves and Estimators for Location and Scale

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

The function rsOptIC computes the optimally robust IC for AL estimators in case of normal scale and (convex) contamination neighborhoods. The definition of these estimators can be found in Rieder (1994) or Kohl (2005), respectively.

1	rsOptIC(r, mean = 0, sd = 1, bUp = 1000, delta = 1e-06, itmax = 100, computeIC = TRUE)

`r`	non-negative real: neighborhood radius.
`mean`	specified mean.
`sd`	specified standard deviation.
`bUp`	positive real: the upper end point of the interval to be searched for the clipping bound b.
`delta`	the desired accuracy (convergence tolerance).
`itmax`	the maximum number of iterations.
`computeIC`	logical: should IC be computed. See details below.

If 'computeIC' is 'FALSE' only the Lagrange multipliers 'A', 'a', and 'b' contained in the optimally robust IC are computed.

If 'computeIC' is 'TRUE' an object of class "ContIC" is returned, otherwise a list of Lagrange multipliers

`A`	standardizing constant
`a`	centering constant
`b`	optimal clipping bound

Matthias Kohl Matthias.Kohl@stamats.de

Rieder, H. (1994) Robust Asymptotic Statistics. New York: Springer.

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

ContIC-class, roblox

IC1 <- rsOptIC(r = 0.1)
distrExOptions("ErelativeTolerance" = 1e-12)
checkIC(IC1)
distrExOptions("ErelativeTolerance" = .Machine$double.eps^0.25) # default
Risks(IC1)
cent(IC1)
clip(IC1)
stand(IC1)
plot(IC1)