| getAsRiskRegTS {ROptRegTS} | R Documentation |
Generic function for the computation of asymptotic risks in case of regression-type models. This function is rarely called directly. It is used by other functions.
getAsRiskRegTS(risk, ErrorL2deriv, Regressor, neighbor, ...)
## S4 method for signature 'asMSE, UnivariateDistribution,
## Distribution, Neighborhood':
getAsRiskRegTS(risk, ErrorL2deriv,
Regressor, neighbor, clip, cent, stand, trafo)
## S4 method for signature 'asMSE, UnivariateDistribution,
## Distribution, Av2CondContNeighborhood':
getAsRiskRegTS(risk, ErrorL2deriv,
Regressor, neighbor, clip, cent, stand, trafo)
## S4 method for signature 'asMSE, EuclRandVariable,
## Distribution, Neighborhood':
getAsRiskRegTS(risk, ErrorL2deriv,
Regressor, neighbor, clip, cent, stand, trafo)
## S4 method for signature 'asBias, UnivariateDistribution,
## UnivariateDistribution, ContNeighborhood':
getAsRiskRegTS(risk, ErrorL2deriv,
Regressor, neighbor, ErrorL2derivDistrSymm, trafo, maxiter, tol)
## S4 method for signature 'asBias, UnivariateDistribution,
## UnivariateDistribution, Av1CondContNeighborhood':
getAsRiskRegTS(risk,
ErrorL2deriv, Regressor, neighbor, ErrorL2derivDistrSymm, trafo, maxiter, tol)
## S4 method for signature 'asBias, UnivariateDistribution,
## UnivariateDistribution, Av1CondTotalVarNeighborhood':
getAsRiskRegTS(risk,
ErrorL2deriv, Regressor, neighbor, ErrorL2derivDistrSymm, trafo, maxiter, tol)
## S4 method for signature 'asBias, UnivariateDistribution,
## MultivariateDistribution, ContNeighborhood':
getAsRiskRegTS(risk,
ErrorL2deriv, Regressor, neighbor, ErrorL2derivDistrSymm, trafo, maxiter, tol)
## S4 method for signature 'asBias, UnivariateDistribution,
## MultivariateDistribution, Av1CondContNeighborhood':
getAsRiskRegTS(risk,
ErrorL2deriv, Regressor, neighbor, ErrorL2derivDistrSymm, trafo, maxiter, tol)
## S4 method for signature 'asBias, UnivariateDistribution,
## MultivariateDistribution,
## Av1CondTotalVarNeighborhood':
getAsRiskRegTS(risk,
ErrorL2deriv, Regressor, neighbor, ErrorL2derivDistrSymm, trafo, maxiter, tol)
## S4 method for signature 'asBias, UnivariateDistribution,
## Distribution, Av2CondContNeighborhood':
getAsRiskRegTS(risk,
ErrorL2deriv, Regressor, neighbor, ErrorL2derivDistrSymm, trafo, maxiter, tol)
## S4 method for signature 'asBias, RealRandVariable,
## Distribution, ContNeighborhood':
getAsRiskRegTS(risk,
ErrorL2deriv, Regressor, neighbor, ErrorDistr, trafo, z.start, A.start, maxiter, tol)
## S4 method for signature 'asBias, RealRandVariable,
## Distribution, Av1CondContNeighborhood':
getAsRiskRegTS(risk,
ErrorL2deriv, Regressor, neighbor, ErrorDistr, trafo, z.start, A.start, maxiter, tol)
## S4 method for signature 'asUnOvShoot,
## UnivariateDistribution, UnivariateDistribution,
## UncondNeighborhood':
getAsRiskRegTS(risk,
ErrorL2deriv, Regressor, neighbor, clip, cent, stand)
## S4 method for signature 'asUnOvShoot,
## UnivariateDistribution, UnivariateDistribution,
## CondNeighborhood':
getAsRiskRegTS(risk,
ErrorL2deriv, Regressor, neighbor, clip, cent, stand)
risk |
object of class "asRisk". |
ErrorL2deriv |
L2-derivative of ErrorDistr. |
Regressor |
regressor. |
neighbor |
object of class "Neighborhood". |
... |
additional parameters. |
clip |
optimal clipping bound. |
cent |
optimal centering constant/function. |
stand |
standardizing matrix. |
trafo |
matrix: transformation of the parameter. |
ErrorDistr |
error distribution. |
ErrorL2derivDistrSymm |
symmetry of ErrorL2derivDistr. |
maxiter |
the maximum number of iterations |
tol |
the desired accuracy (convergence tolerance). |
z.start |
initial value for the centering constant/function. |
A.start |
initial value for the standardizing matrix. |
The asymptotic risk is computed.
getInfRobRegTypeIC. getInfRobRegTypeIC. getInfRobRegTypeIC. getInfRobRegTypeIC. getInfRobRegTypeIC. getInfRobRegTypeIC. getInfRobRegTypeIC. getInfRobRegTypeIC. getInfRobRegTypeIC. getInfRobRegTypeIC. getInfRobRegTypeIC. getInfRobRegTypeIC. getInfRobRegTypeIC. getInfRobRegTypeIC. 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.