getInfGamma {ROptEstOld} | R Documentation |
Generic function for the computation of the optimal clipping bound.
This function is rarely called directly. It is called by getInfClip
to compute optimally robust ICs.
getInfGamma(L2deriv, risk, neighbor, ...) ## S4 method for signature 'UnivariateDistribution, asMSE, ## ContNeighborhood': getInfGamma(L2deriv, risk, neighbor, cent, clip) ## S4 method for signature 'UnivariateDistribution, ## asGRisk, TotalVarNeighborhood': getInfGamma(L2deriv, risk, neighbor, cent, clip) ## S4 method for signature 'RealRandVariable, asMSE, ## ContNeighborhood': getInfGamma(L2deriv, risk, neighbor, Distr, stand, cent, clip) ## S4 method for signature 'UnivariateDistribution, ## asUnOvShoot, ContNeighborhood': getInfGamma(L2deriv, risk, neighbor, cent, clip)
L2deriv |
L2-derivative of some L2-differentiable family of probability measures. |
risk |
object of class "RiskType" . |
neighbor |
object of class "Neighborhood" . |
... |
additional parameters |
cent |
optimal centering constant. |
clip |
optimal clipping bound. |
stand |
standardizing matrix. |
Distr |
object of class "Distribution" . |
The function is used in case of asymptotic G-risks; confer Ruckdeschel and Rieder (2004).
getInfClip
. getInfClip
. getInfClip
. getInfClip
. Matthias Kohl Matthias.Kohl@stamats.de
Rieder, H. (1980) Estimates derived from robust tests. Ann. Stats. 8: 106–115.
Rieder, H. (1994) Robust Asymptotic Statistics. New York: Springer.
Ruckdeschel, P. and Rieder, H. (2004) Optimal Influence Curves for General Loss Functions. Statistics & Decisions (submitted).
Kohl, M. (2005) Numerical Contributions to the Asymptotic Theory of Robustness. Bayreuth: Dissertation.
asGRisk-class
, asMSE-class
,
asUnOvShoot-class
, ContIC-class
,
TotalVarIC-class