rgsOptIC.ALs {RobRex} | R Documentation |
The function rgsOptIC.ALs
computes the optimally robust IC
for ALs estimators in case of linear regression with unknown
scale and (convex) contamination neighborhoods where the
regressor is random; confer Subsection 7.3.1 of Kohl (2005).
rgsOptIC.ALs(r, K, A.rg.start, b.rg.Up = 1000, delta = 1e-06, itmax = 50, check = FALSE)
r |
non-negative real: neighborhood radius. |
K |
object of class "Distribution" . |
A.rg.start |
positive definite and symmetric matrix: starting value for the standardizing matrix of the regression part. |
b.rg.Up |
positive real: the upper end point of the interval to be searched for b.rg. |
delta |
the desired accuracy (convergence tolerance). |
itmax |
the maximum number of iterations. |
check |
logical. Should constraints be checked. |
If A.rg.start
is missing, the inverse of the
second moment matrix of K
is used.
Object of class "ContIC"
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
## code takes some time ## Not run: K <- DiscreteDistribution(1:5) # = Unif({1,2,3,4,5}) IC1 <- rgsOptIC.ALs(r = 0.1, K = K) checkIC(IC1) Risks(IC1) Infos(IC1) ## End(Not run)