Description Usage Arguments Details Value Author(s) References See Also Examples
The function rgsOptIC.AL
computes the optimally robust IC
for AL estimators in case of linear regression with unknown
scale and (convex) contamination neighborhoods where the
regressor is random; confer Subsubsection 7.2.1.1 of Kohl (2005).
1 2 | rgsOptIC.AL(r, K, theta, scale = 1, A.rg.start, a.sc.start = 0, A.sc.start = 0.5,
bUp = 1000, delta = 1e-06, itmax = 50, check = FALSE)
|
r |
non-negative real: neighborhood radius. |
K |
object of class |
theta |
specified regression parameter. |
scale |
specified error scale. |
A.rg.start |
positive definite and symmetric matrix: starting value for the standardizing matrix of the regression 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. |
If theta
is missing, it is set to 0.
If A.rg.start
is missing, the inverse of the
second moment matrix of K
is used.
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 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.
1 2 3 4 5 6 7 | K <- DiscreteDistribution(1:5) # = Unif({1,2,3,4,5})
IC1 <- rgsOptIC.AL(r = 0.1, K = K)
checkIC(IC1)
Risks(IC1)
cent(IC1)
clip(IC1)
stand(IC1)
|
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