rlsOptIC.AL {RobLox} | R Documentation |
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).
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)
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. |
The Lagrange multipliers contained in the expression
of the optimally robust IC can be accessed via the
accessor functions cent
, clip
and stand
.
Object of class "ContIC"
Matthias Kohl robast@gmx.de
Rieder, H. (1994) Robust Asymptotic Statistics. New York: Springer.
Kohl, M. (2005) Numerical Contributions to the Asymptotic Theory of Robustness. Bayreuth: Dissertation.
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)