oneStepEstimator {RobAStBase} | R Documentation |
Function for the computation of one-step estimates
Description
Function for the computation of one-step estimates.
Usage
oneStepEstimator(x, IC, start = NULL,
useLast = getRobAStBaseOption("kStepUseLast"),
withUpdateInKer = getRobAStBaseOption("withUpdateInKer"),
IC.UpdateInKer = getRobAStBaseOption("IC.UpdateInKer"),
na.rm = TRUE, startArgList = NULL, withMakeIC = FALSE, ...,
E.argList = NULL)
Arguments
x |
sample |
IC |
object of class |
start |
initial estimate (for full parameter,i.e. in dimension |
useLast |
which parameter estimate (initial estimate or
one-step estimate) shall be used to fill the slots |
withUpdateInKer |
if there is a non-trivial trafo in the model with matrix |
IC.UpdateInKer |
if there is a non-trivial trafo in the model with matrix |
na.rm |
logical: if |
startArgList |
a list of arguments to be given to argument |
withMakeIC |
logical; if |
... |
additional arguments |
E.argList |
|
Details
Given an initial estimation start
, a sample x
and an influence curve IC
the corresponding one-step
estimator is computed.
In case IC
is an object of class "IC"
the slots asvar
and asbias
of the return
value are filled (based on the initial estimate).
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 one-step estimate.
Value
Object of class "kStepEstimate"
Author(s)
Matthias Kohl Matthias.Kohl@stamats.de,
Peter Ruckdeschel peter.ruckdeschel@uni-oldenburg.de
References
Rieder, H. (1994) Robust Asymptotic Statistics. New York: Springer.
Kohl, M. (2005) Numerical Contributions to the Asymptotic Theory of Robustness. Bayreuth: Dissertation.
See Also
InfluenceCurve-class
, kStepEstimate-class