Description Usage Arguments Details Value Methods Author(s) References See Also Examples
Generic function for the computation of optimally robust ICs.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22  optIC(model, risk, ...)
## S4 method for signature 'InfRobModel,asRisk'
optIC(model, risk, z.start = NULL, A.start = NULL,
upper = 1e4, lower = 1e4,
OptOrIter = "iterate", maxiter = 50,
tol = .Machine$double.eps^0.4, warn = TRUE,
noLow = FALSE, verbose = NULL, ...,
.withEvalAsVar = TRUE,
returnNAifProblem = FALSE)
## S4 method for signature 'InfRobModel,asUnOvShoot'
optIC(model, risk, upper = 1e4,
lower = 1e4, maxiter = 50,
tol = .Machine$double.eps^0.4, warn = TRUE,
verbose = NULL)
## S4 method for signature 'FixRobModel,fiUnOvShoot'
optIC(model, risk, sampleSize, upper = 1e4, lower = 1e4,
maxiter = 50, tol = .Machine$double.eps^0.4,
warn = TRUE, Algo = "A", cont = "left",
verbose = NULL)

model 
probability model. 
risk 
object of class 
... 
additional parameters. 
z.start 
initial value for the centering constant. 
A.start 
initial value for the standardizing matrix. 
upper 
upper bound for the optimal clipping bound. 
lower 
lower bound for the optimal clipping bound. 
maxiter 
the maximum number of iterations. 
tol 
the desired accuracy (convergence tolerance). 
warn 
logical: print warnings. 
sampleSize 
integer: sample size. 
Algo 
"A" or "B". 
cont 
"left" or "right". 
noLow 
logical: is lower case to be computed? 
OptOrIter 
character; which method to be used for determining Lagrange
multipliers 
verbose 
logical: if 
.withEvalAsVar 
logical (of length 1):
if 
returnNAifProblem 
logical (of length 1):
if 
In case of the finitesample risk "fiUnOvShoot"
one can choose
between two algorithms for the computation of this risk where the least favorable
contamination is assumed to be left or right of some bound. For more details
we refer to Section 11.3 of Kohl (2005).
Some optimally robust IC is computed.
computes optimally robust influence curve for robust models with infinitesimal neighborhoods and various asymptotic risks.
computes optimally robust influence curve for robust models with infinitesimal neighborhoods and asymptotic under/overshoot risk.
computes optimally robust influence curve for robust models with fixed neighborhoods and finitesample under/overshoot risk.
Matthias Kohl [email protected]
Huber, P.J. (1968) Robust Confidence Limits. Z. Wahrscheinlichkeitstheor. Verw. Geb. 10:269–278.
Kohl, M. (2005) Numerical Contributions to the Asymptotic Theory of Robustness. Bayreuth: Dissertation.
Kohl, M. and Ruckdeschel, P. (2010): R package distrMod: ObjectOriented Implementation of Probability Models. J. Statist. Softw. 35(10), 1–27
Kohl, M. and Ruckdeschel, P., and Rieder, H. (2010): Infinitesimally Robust Estimation in General Smoothly Parametrized Models. Stat. Methods Appl., 19, 333–354.
Rieder, H. (1980) Estimates derived from robust tests. Ann. Stats. 8: 106–115.
Rieder, H. (1994) Robust Asymptotic Statistics. New York: Springer.
Rieder, H., Kohl, M. and Ruckdeschel, P. (2008) The Costs of not Knowing the Radius. Statistical Methods and Applications 17(1) 1340.
Rieder, H., Kohl, M. and Ruckdeschel, P. (2001) The Costs of not Knowing the Radius. Appeared as discussion paper Nr. 81. SFB 373 (Quantification and Simulation of Economic Processes), Humboldt University, Berlin; also available under www.unibayreuth.de/departments/math/org/mathe7/RIEDER/pubs/RR.pdf
InfluenceCurveclass
, RiskTypeclass
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