R语言 ROptRegTS包 getInfClipRegTS()函数中文帮助文档(中英文对照)

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getInfClipRegTS(ROptRegTS)
getInfClipRegTS()所属R语言包：ROptRegTS

                                        Generic Function for the Computation of the Optimal Clipping Bound
                                         通用功能的最优裁剪绑定的计算

                                         译者：生物统计家园网 机器人LoveR

描述----------Description----------

Generic function for the computation of the optimal clipping bound/function. This function is rarely called directly. It is used to  compute optimally robust ICs in case infinitesimal models.
计算的最佳剪辑绑定/功能的通用功能。很少直接调用此函数。它是用来计算最佳鲁棒的IC的情况下无穷小的模型。


用法----------Usage----------


getInfClipRegTS(clip, ErrorL2deriv, Regressor, risk, neighbor, ...)

## S4 method for signature 'numeric,UnivariateDistribution,Distribution,asMSE,Neighborhood'
getInfClipRegTS(clip,
                ErrorL2deriv, Regressor, risk, neighbor, z.comp, stand, cent)

## S4 method for signature 'numeric,UnivariateDistribution,Distribution,asMSE,Av1CondTotalVarNeighborhood'
getInfClipRegTS(clip,
                ErrorL2deriv, Regressor, risk, neighbor, z.comp, stand, cent)

## S4 method for signature 'numeric,EuclRandVariable,Distribution,asMSE,Neighborhood'
getInfClipRegTS(clip, ErrorL2deriv,
                Regressor, risk, neighbor, ErrorDistr, stand, cent, trafo)

## S4 method for signature 'numeric,UnivariateDistribution,UnivariateDistribution,asUnOvShoot,UncondNeighborhood'
getInfClipRegTS(clip,
                ErrorL2deriv, Regressor, risk, neighbor, z.comp, cent)

## S4 method for signature 'numeric,UnivariateDistribution,numeric,asUnOvShoot,CondNeighborhood'
getInfClipRegTS(clip,
                ErrorL2deriv, Regressor, risk, neighbor)



参数----------Arguments----------

参数：clip
optimal clipping bound.
最佳剪裁的约束。


参数：ErrorL2deriv
L2-derivative of ErrorDistr.
L2衍生ErrorDistr。


参数：Regressor
regressor.
回归量。


参数：risk
object of class "RiskType".
对象类"RiskType"。


参数：neighbor
object of class "Neighborhood".
对象类"Neighborhood"。


参数：...
additional parameters.
附加参数。


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：z.comp
which components of the centering constant/function  have to be computed.
定心的常数/功能的组成部分有以被计算。


参数：ErrorDistr
error distribution.
误差分布。


参数：trafo
matrix: transformation of the parameter.   
矩阵变换的参数。


值----------Value----------

The optimal clipping bound/function is computed.
的最佳剪辑绑定/函数计算。


方法----------Methods----------

  


clip = "numeric", ErrorL2deriv = "UnivariateDistribution", Regressor = "Distribution", risk = "asMSE", neighbor = "Neighborhood"  optimal clipping bound for asymtotic mean square error.
夹=“数字”，ErrorL2deriv =“UnivariateDistribution”，REGRESSOR =“分配”，风险的“asMSE”，邻居=“邻居”最佳裁剪开往渐进的均方误差。




clip = "numeric", ErrorL2deriv = "UnivariateDistribution", Regressor = "Distribution", risk = "asMSE", neighbor = "Av1CondTotalVarNeighborhood"  optimal clipping bound for asymtotic mean square error.
夹=“数字”，ErrorL2deriv =“UnivariateDistribution”，REGRESSOR =“分配”，风险的“asMSE”，邻居=“Av1CondTotalVarNeighborhood”最佳裁剪开往渐进的均方误差。




clip = "numeric", ErrorL2deriv = "EuclRandVariable", Regressor = "Distribution", risk = "asMSE", neighbor = "Neighborhood"  optimal clipping bound for asymtotic mean square error.
夹=“数字”，ErrorL2deriv =“EuclRandVariable”，REGRESSOR =“分配”，风险=的“asMSE”邻居“邻居”最佳裁剪开往渐进的均方误差。




clip = "numeric", ErrorL2deriv = "UnivariateDistribution", Regressor = "UnivariateDistribution", risk = "asUnOvShoot", neighbor = "UncondNeighborhood"  optimal clipping bound for asymtotic under-/overshoot risk.
夹=“数字”，ErrorL2deriv =“UnivariateDistribution”，REGRESSOR =“UnivariateDistribution”的风险=“asUnOvShoot”，邻居=的“UncondNeighborhood”最佳裁剪为渐进的under-/overshoot风险约束。




clip = "numeric", ErrorL2deriv = "UnivariateDistribution", Regressor = "numeric", risk = "asUnOvShoot", neighbor = "CondNeighborhood"  optimal clipping function for asymtotic under-/overshoot risk.   
片段=“数值”，ErrorL2deriv =“UnivariateDistribution”，REGRESSOR =“数字”，风险=的“asUnOvShoot”邻居“CondNeighborhood”最佳剪辑功能为渐进的under-/overshoot风险。


（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&ndash;115.
Rieder, H. (1994) Robust Asymptotic Statistics. New York: Springer.
Kohl, M. (2005) Numerical Contributions to the Asymptotic Theory of Robustness.  Bayreuth: Dissertation.

参见----------See Also----------

ContIC-class, TotalVarIC-class,  Av1CondContIC-class, Av2CondContIC-class,  Av1CondTotalVarIC-class, CondContIC-class,
ContIC-class，TotalVarIC-class，Av1CondContIC-class，Av2CondContIC-class，Av1CondTotalVarIC-class，CondContIC-class，

转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。


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