optRisk: Generic function for the computation of the minimal risk

Description

Generic function for the computation of the optimal (i.e., minimal) risk for a probability model.

Usage

optRisk(model, risk, ...)
"optRisk"(model, risk)
"optRisk"(model, risk, z.start = NULL, A.start = NULL, upper = 1e4, maxiter = 50, tol = .Machine$double.eps^0.4, warn = TRUE, noLow = FALSE)
"optRisk"(model, risk, sampleSize, upper = 1e4, maxiter = 50, tol = .Machine$double.eps^0.4, warn = TRUE, Algo = "A", cont = "left")

Arguments

model

probability model

risk

object of class RiskType

...

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.

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?

Value

Methods

Details

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 bound. For more details we refer to Section 11.3 of Kohl (2005).

References

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.

Examples

Run this code

optRisk(model = NormLocationScaleFamily(), risk = asCov())

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