kStepEstimator {RobAStBase} | R Documentation |
Generic function for the computation of k-step estimates.
kStepEstimator(x, IC, start, ...) ## S4 method for signature 'numeric, IC, numeric': kStepEstimator(x, IC, start, steps = 1L, useLast = getRobAStBaseOption("kStepUseLast")) ## S4 method for signature 'matrix, IC, numeric': kStepEstimator(x, IC, start, steps = 1L, useLast = getRobAStBaseOption("kStepUseLast")) ## S4 method for signature 'numeric, IC, Estimate': kStepEstimator(x, IC, start, steps = 1L, useLast = getRobAStBaseOption("kStepUseLast")) ## S4 method for signature 'matrix, IC, Estimate': kStepEstimator(x, IC, start, steps = 1L, useLast = getRobAStBaseOption("kStepUseLast"))
x |
sample |
IC |
object of class "IC" |
start |
initial estimate |
steps |
integer: number of steps |
useLast |
which parameter estimate (initial estimate or
k-step estimate) shall be used to fill the slots pIC ,
asvar and asbias of the return value. |
... |
additional parameters |
Given an initial estimation start
, a sample x
and an influence curve IC
the corresponding k-step
estimator is computed.
The default value of argument useLast
is set by the
global option kStepUseLast
which by default is set to
FALSE
. In case of general models useLast
remains unchanged during the computations. However, if
slot CallL2Fam
of IC
generates an object of
class "L2GroupParamFamily"
the value of useLast
is changed to TRUE
.
Explicitly setting useLast
to TRUE
should
be done with care as in this situation the influence curve
is re-computed using the value of the one-step estimate
which may take quite a long time depending on the model.
If useLast
is set to TRUE
and slot modifyIC
of IC
is filled with some function (which can be
used to re-compute the IC for a different parameter), the
computation of asvar
, asbias
and IC
is
based on the k-step estimate.
Object of class "kStepEstimate"
.
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.