optIC-methods: Methods for Function optIC in Package 'ROptRegTS'
In ROptRegTS: Optimally Robust Estimation for Regression-Type Models

Description Usage Arguments Details Value Methods Author(s) References See Also

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.

model = "L2RegTypeFamily", risk = "asCov": computes classical optimal influence curve for L2 differentiable regression-type families.
model = "InfRobRegTypeModel", risk = "asRisk": computes optimally robust influence curve for robust regression-type models with infinitesimal neighborhoods and various asymptotic risks.
model = "InfRobRegTypeModel", risk = "asUnOvShoot": computes optimally robust influence curve for robust regression-type models with infinitesimal neighborhoods and asymptotic under-/overshoot risk.
model = "FixRobRegTypeModel", risk = "fiUnOvShoot": computes optimally robust influence curve for robust regression-type models with fixed neighborhoods and finite-sample under-/overshoot risk.