Description Usage Arguments Value Author(s) References See Also
Functions to determine Lagrange multipliers A
and a
in a Hampel problem or in a(n) (inner) loop in a MSE problem; can be done
either by optimization or by fixed point iteration. These functions are
rarely called directly.
1 2 3 4 5 6 7 8 9 | getLagrangeMultByIter(b, L2deriv, risk, trafo,
neighbor, biastype, normtype, Distr,
a.start, z.start, A.start, w.start, std, z.comp,
A.comp, maxiter, tol, verbose = NULL,
warnit = TRUE, ...)
getLagrangeMultByOptim(b, L2deriv, risk, FI, trafo,
neighbor, biastype, normtype, Distr,
a.start, z.start, A.start, w.start, std, z.comp,
A.comp, maxiter, tol, verbose = NULL, ...)
|
b |
numeric; (>b_min; clipping bound for which the Lagrange multipliers are searched |
L2deriv |
L2-derivative of some L2-differentiable family of probability measures. |
risk |
object of class |
FI |
matrix: Fisher information. |
trafo |
matrix: transformation of the parameter. |
neighbor |
object of class |
biastype |
object of class |
normtype |
object of class |
Distr |
object of class |
a.start |
initial value for the centering constant (in |
z.start |
initial value for the centering constant (in |
A.start |
initial value for the standardizing matrix. |
w.start |
initial value for the weight function. |
std |
matrix of (or which may coerced to) class
|
z.comp |
logical vector: indication which components of the centering constant have to be computed. |
A.comp |
matrix: indication which components of the standardizing matrix have to be computed. |
maxiter |
the maximum number of iterations. |
tol |
the desired accuracy (convergence tolerance). |
verbose |
logical: if |
warnit |
logical: if |
... |
additional parameters for |
a list with items
A |
Lagrange multiplier |
a |
Lagrange multiplier |
z |
Lagrange multiplier |
w |
weight function involving Lagrange multipliers |
biastype |
(possibly modified) bias type |
normtype |
(possibly modified) norm type |
normtype.old |
(possibly modified) norm type |
risk |
(possibly [norm-]modified) risk |
std |
(possibly modified) argument |
iter |
number of iterations needed |
prec |
precision achieved |
b |
used clippng height |
call |
call with which either |
Peter Ruckdeschel peter.ruckdeschel@uni-oldenburg.de
Rieder, H. (1980) Estimates derived from robust tests. Ann. Stats. 8: 106-115.
Rieder, H. (1994) Robust Asymptotic Statistics. New York: Springer.
Ruckdeschel, P. and Rieder, H. (2004) Optimal Influence Curves for General Loss Functions. Statistics & Decisions 22: 201-223.
Ruckdeschel, P. (2005) Optimally One-Sided Bounded Influence Curves. Mathematical Methods in Statistics 14(1), 105-131.
Kohl, M. (2005) Numerical Contributions to the Asymptotic Theory of Robustness. Bayreuth: Dissertation.
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