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 
L2derivative of some L2differentiable 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 [email protected]
Rieder, H. (1980) Estimates derived from robust tests. Ann. Stats. 8: 106115.
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: 201223.
Ruckdeschel, P. (2005) Optimally OneSided Bounded Influence Curves. Mathematical Methods in Statistics 14(1), 105131.
Kohl, M. (2005) Numerical Contributions to the Asymptotic Theory of Robustness. Bayreuth: Dissertation.
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