Description Usage Arguments Value Author(s) References See Also Examples
The function rgsOptIC.Ms
computes the optimally robust
conditionally centered IC for Ms estimators in case of linear
regression with unknown scale and average conditional (convex)
contamination neighborhoods where the regressor is random;
confer Subsection 7.3.2 of Kohl (2005).
1 2 3 | rgsOptIC.Ms(r, K, a1.x.start, a3.start = 0.25, b.sc.start = 1.5,
bUp = 1000, ggLo = 0.5, ggUp = 1, delta = 1e-06,
itmax = 1000, check = FALSE)
|
r |
non-negative real: neighborhood radius. |
K |
object of class |
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.x.start |
real: starting value for the Lagrange multiplier function alpha_1(x). |
a3.start |
real: starting value for Lagrange multiplier alpha_3. |
b.sc.start |
positive real: starting value for the clipping bound b_sc. |
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. |
Object of class "CondIC"
Matthias Kohl Matthias.Kohl@stamats.de
Kohl, M. (2005) Numerical Contributions to the Asymptotic Theory of Robustness. Bayreuth: Dissertation.
1 2 3 4 5 6 7 8 | ## code takes some time
## Not run:
K <- DiscreteDistribution(1:5) # = Unif({1,2,3,4,5})
IC1 <- rgsOptIC.Ms(r = 0.1, K = K)
checkIC(IC1)
Risks(IC1)
## End(Not run)
|
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