Description Usage Arguments Details Value Methods Author(s) References See Also
Methods for function optIC
in package ROptRegTS.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16  ## S4 method for signature 'L2RegTypeFamily,asCov'
optIC(model, risk)
## S4 method for signature 'InfRobRegTypeModel,asRisk'
optIC(model, risk, z.start = NULL,
A.start = NULL, upper = 1e4, maxiter = 50,
tol = .Machine$double.eps^0.4, warn = TRUE)
## S4 method for signature 'InfRobRegTypeModel,asUnOvShoot'
optIC(model, risk, upper = 1e4,
maxiter = 50, tol = .Machine$double.eps^0.4, warn = TRUE)
## S4 method for signature 'FixRobRegTypeModel,fiUnOvShoot'
optIC(model, risk, sampleSize,
upper = 1e4, maxiter = 50, tol = .Machine$double.eps^0.4,
warn = TRUE, Algo = "A", cont = "left")

model 
probability model. 
risk 
object of class 
z.start 
initial value for the centering constant. 
A.start 
initial value for the standardizing matrix. 
upper 
upper 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". 
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 boundary
curve. For more details we refer to Subsections 12.1.3 and 12.2.3 of Kohl (2005).
Some optimally robust IC is computed.
computes classical optimal influence curve for L2 differentiable regressiontype families.
computes optimally robust influence curve for robust regressiontype models with infinitesimal neighborhoods and various asymptotic risks.
computes optimally robust influence curve for robust regressiontype models with infinitesimal neighborhoods and asymptotic under/overshoot risk.
computes optimally robust influence curve for robust regressiontype models with fixed neighborhoods and finitesample under/overshoot risk.
Matthias Kohl Matthias.Kohl@stamats.de
Huber, P.J. (1968) Robust Confidence Limits. Z. Wahrscheinlichkeitstheor. Verw. Geb. 10:269–278.
Rieder, H. (1980) Estimates derived from robust tests. Ann. Stats. 8: 106–115.
Rieder, H. (1994) Robust Asymptotic Statistics. New York: Springer.
Kohl, M. (2005) Numerical Contributions to the Asymptotic Theory of Robustness. Bayreuth: Dissertation.
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