rgsOptIC.ALc {RobRex} | R Documentation |
The function rgsOptIC.ALc
computes the optimally robust
conditionally centered IC for AL estimators in case of linear
regression with unknown scale and average conditional
(convex) contamination neighborhoods where the regressor is
random; confer Subsubsection 7.2.1.2 of Kohl (2005).
rgsOptIC.ALc(r, K, theta, scale = 1, A.rg.start, a.sc.start, A.sc.start = 0.5, bUp = 1000, delta = 1e-06, itmax = 50, check = FALSE)
r |
non-negative real: neighborhood radius. |
K |
object of class "DiscreteDistribution" or
object of class "DisreteMVDistribution" . |
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 vector: starting values for centering function 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. In case
a.sc.start
is missing, it is set to a null
vector with length of the support of K
.
Object of class "Av1CondContIC"
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.
Av1CondContIC-class
K <- DiscreteDistribution(1:5) # = Unif({1,2,3,4,5}) IC1 <- rgsOptIC.ALc(r = 0.1, K = K) checkIC(IC1) Risks(IC1)