rgsOptIC.M | R Documentation |
The function rgsOptIC.M
computes the optimally robust IC
for M 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).
rgsOptIC.M(r, K, A.start, gg.start = 0.6, a1.start = -0.25,
a3.start = 0.25, B.start, bUp = 1000, delta = 1e-05,
MAX = 100, itmax = 1000, check = FALSE)
r |
non-negative real: neighborhood radius. |
K |
object of class |
A.start |
positive definite and symmetric matrix: starting value for the standardizing matrix of the regression part. |
gg.start |
positive real: starting value for
the standardizing constant |
a1.start |
real: starting value for
Lagrange multiplier |
a3.start |
real: starting value for
Lagrange multiplier |
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). |
MAX |
if A or |
itmax |
the maximum number of iterations. |
check |
logical. Should constraints be checked. |
The computation of the optimally robust IC for M estimators
is based on optim
where MAX
is used to
control the constraints on A and \gamma
.
Object of class "IC"
Matthias Kohl Matthias.Kohl@stamats.de
Kohl, M. (2005) Numerical Contributions to the Asymptotic Theory of Robustness. Bayreuth: Dissertation.
IC-class
## code takes some time
## Not run:
K <- DiscreteDistribution(1:5) # = Unif({1,2,3,4,5})
IC1 <- rgsOptIC.M(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.