getAsRiskRegTS(ROptRegTS)
getAsRiskRegTS()所属R语言包：ROptRegTS
Generic Function for Computation of Asymptotic Risks in case of Regression-Type Models
通用函数计算的情况下，回归类模型的渐近风险
译者：生物统计家园网 机器人LoveR
描述----------Description----------
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.
回归模型的渐近风险的情况下计算的通用功能。很少直接调用此函数。它被用来由其它函数。
用法----------Usage----------
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)
参数----------Arguments----------
参数：risk
object of class "asRisk".
对象类"asRisk"。
参数：ErrorL2deriv
L2-derivative of ErrorDistr.
L2衍生ErrorDistr。
参数：Regressor
regressor.
回归量。
参数：neighbor
object of class "Neighborhood".
对象类"Neighborhood"。
参数：...
additional parameters.
附加参数。
参数：clip
optimal clipping bound.
最佳剪裁的约束。
参数：cent
optimal centering constant/function.
最佳定心常数/功能。
参数：stand
standardizing matrix.
规范矩阵。
参数：trafo
matrix: transformation of the parameter.
矩阵变换的参数。
参数：ErrorDistr
error distribution.
误差分布。
参数：ErrorL2derivDistrSymm
symmetry of ErrorL2derivDistr.
对称的ErrorL2derivDistr。
参数：maxiter
the maximum number of iterations
最大迭代次数
参数：tol
the desired accuracy (convergence tolerance).
所需的精度（收敛宽容）。
参数：z.start
initial value for the centering constant/function.
中心的常数/函数的初始值。
参数：A.start
initial value for the standardizing matrix.
标准化矩阵的初始值。
值----------Value----------
The asymptotic risk is computed.
的渐近计算风险。
方法----------Methods----------
risk = "asMSE", ErrorL2deriv = "UnivariateDistribution", Regressor = "Distribution", neighbor = "Neighborhood" computes asymptotic mean square error in methods for function getInfRobRegTypeIC.
风险=“asMSE”的，ErrorL2deriv =“UnivariateDistribution”，回归量=“分配”，邻居=“邻居”计算函数getInfRobRegTypeIC渐近均方误差的方法。
risk = "asMSE", ErrorL2deriv = "UnivariateDistribution", Regressor = "Distribution", neighbor = "Av2CondContNeighborhood" computes asymptotic mean square error in methods for function getInfRobRegTypeIC.
风险=“asMSE”的，ErrorL2deriv =“UnivariateDistribution”，回归量=“分配”，邻居=“Av2CondContNeighborhood”计算函数getInfRobRegTypeIC渐近均方误差的方法。
risk = "asMSE", ErrorL2deriv = "EuclRandVariable", Regressor = "Distribution", neighbor = "Neighborhood" computes asymptotic mean square error in methods for function getInfRobRegTypeIC.
风险=“asMSE”，ErrorL2deriv =“EuclRandVariable”，REGRESSOR =“分配”邻居“邻居”计算功能getInfRobRegTypeIC渐近均方误差的方法。
risk = "asBias", ErrorL2deriv = "UnivariateDistribution", Regressor = "UnivariateDistribution", neighbor = "ContNeighborhood" computes standardized asymptotic bias in methods for function getInfRobRegTypeIC.
风险=“asBias”，ErrorL2deriv =“UnivariateDistribution”，REGRESSOR =“UnivariateDistribution”，邻居=“ContNeighborhood”计算标准化的渐近偏差的方法函数getInfRobRegTypeIC。
risk = "asBias", ErrorL2deriv = "UnivariateDistribution", Regressor = "UnivariateDistribution", neighbor = "Av1CondContNeighborhood" computes standardized asymptotic bias in methods for function getInfRobRegTypeIC.
风险=“asBias”，ErrorL2deriv =“UnivariateDistribution”，REGRESSOR =“UnivariateDistribution”，邻居=“Av1CondContNeighborhood”计算标准化的渐近偏差的方法函数getInfRobRegTypeIC。
risk = "asBias", ErrorL2deriv = "UnivariateDistribution", Regressor = "UnivariateDistribution", neighbor = "Av1CondTotalVarNeighborhood" computes standardized asymptotic bias in methods for function getInfRobRegTypeIC.
风险=“asBias”，ErrorL2deriv =“UnivariateDistribution”，REGRESSOR =“UnivariateDistribution”，邻居=“Av1CondTotalVarNeighborhood”计算标准化的渐近偏差的方法函数getInfRobRegTypeIC。
risk = "asBias", ErrorL2deriv = "UnivariateDistribution", Regressor = "MultivariateDistribution", neighbor = "ContNeighborhood" computes standardized asymptotic bias in methods for function getInfRobRegTypeIC.
风险=“asBias”，ErrorL2deriv =“UnivariateDistribution”，REGRESSOR =“MultivariateDistribution”，邻居=“ContNeighborhood”计算标准化的渐近偏差的方法函数getInfRobRegTypeIC。
risk = "asBias", ErrorL2deriv = "UnivariateDistribution", Regressor = "MultivariateDistribution", neighbor = "Av1CondContNeighborhood" computes standardized asymptotic bias in methods for function getInfRobRegTypeIC.
风险=“asBias”，ErrorL2deriv =“UnivariateDistribution”，REGRESSOR =“MultivariateDistribution”，邻居=“Av1CondContNeighborhood”计算标准化的渐近偏差的方法函数getInfRobRegTypeIC。
risk = "asBias", ErrorL2deriv = "UnivariateDistribution", Regressor = "MultivariateDistribution", neighbor = "Av1CondTotalVarNeighborhood" computes standardized asymptotic bias in methods for function getInfRobRegTypeIC.
风险=“asBias”，ErrorL2deriv =“UnivariateDistribution”，REGRESSOR =“MultivariateDistribution”，邻居=“Av1CondTotalVarNeighborhood”计算标准化的渐近偏差的方法函数getInfRobRegTypeIC。
risk = "asBias", ErrorL2deriv = "UnivariateDistribution", Regressor = "Distribution", neighbor = "Av2CondContNeighborhood" computes standardized asymptotic bias in methods for function getInfRobRegTypeIC.
风险=“asBias”，ErrorL2deriv =“UnivariateDistribution”，REGRESSOR =“分配”，邻居=“Av2CondContNeighborhood”计算标准化的渐近偏差的方法函数getInfRobRegTypeIC。
risk = "asBias", ErrorL2deriv = "RealRandVariable", Regressor = "Distribution", neighbor = "ContNeighborhood" computes standardized asymptotic bias in methods for function getInfRobRegTypeIC.
风险=“asBias”，ErrorL2deriv =“RealRandVariable”，REGRESSOR =“分配”，邻居=“ContNeighborhood”计算标准的渐近偏差的方法函数getInfRobRegTypeIC。
risk = "asBias", ErrorL2deriv = "RealRandVariable", Regressor = "Distribution", neighbor = "Av1CondContNeighborhood" computes standardized asymptotic bias in methods for function getInfRobRegTypeIC.
风险=“asBias”，ErrorL2deriv =“RealRandVariable”，REGRESSOR =“分配”，邻居=“Av1CondContNeighborhood”计算标准的渐近偏差的方法函数getInfRobRegTypeIC。
risk = "asUnOvShoot", ErrorL2deriv = "UnivariateDistribution", Regressor = "UnivariateDistribution", neighbor = "UncondNeighborhood" computes asymptotic under-/overshoot risk in methods for function getInfRobRegTypeIC.
风险=“asUnOvShoot”，ErrorL2deriv =“UnivariateDistribution”，REGRESSOR，邻居=“UnivariateDistribution”=“UncondNeighborhood”，计算渐近under-/overshoot风险为函数getInfRobRegTypeIC的方法。
risk = "asUnOvShoot", ErrorL2deriv = "UnivariateDistribution", Regressor = "UnivariateDistribution", neighbor = "CondNeighborhood" computes asymptotic under-/overshoot risk in methods for function getInfRobRegTypeIC.
风险=“asUnOvShoot”，ErrorL2deriv =“UnivariateDistribution”，REGRESSOR，邻居=“UnivariateDistribution”=“CondNeighborhood”，计算渐近under-/overshoot风险为函数getInfRobRegTypeIC的方法。
（作者）----------Author(s)----------
Matthias Kohl <a href="mailto:Matthias.Kohl@stamats.de">Matthias.Kohl@stamats.de</a>
参考文献----------References----------
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.
参见----------See Also----------
asRisk-class
asRisk-class
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注：
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发表于 2012-9-27 23:22:39
