optIC-methods {ROptRegTS} | R Documentation |
Methods for function optIC
in package ROptRegTS.
## 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 "RiskType" . |
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 finite-sample 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.
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.