Description Usage Arguments Value Author(s) References See Also Examples
The function rgsOptIC.MK
computes the optimally robust IC
for MK estimators in case of linear regression with unknown
scale and (convex) contamination neighborhoods where the
regressor is random; confer Subsubsection 7.2.2.1 of Kohl (2005).
1 2 | rgsOptIC.MK(r, K, ggLo = 0.5, ggUp = 1, a1.start = -0.25, a3.start = 0.25,
B.start, bUp = 1000, 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.start |
real: starting value for Lagrange multiplier alpha_1. |
a3.start |
real: starting value for Lagrange multiplier alpha_3. |
B.start |
symmetric matrix: starting value for Lagrange multiplier B. |
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 "IC"
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.MK(r = 0.1, K = K)
checkIC(IC1)
Risks(IC1)
## End(Not run)
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.