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

loveR

getAsRisk(ROptEstOld)
getAsRisk()所属R语言包：ROptEstOld

                                        Generic Function for Computation of Asymptotic Risks
                                         通用函数计算的渐近风险

                                         译者：生物统计家园网 机器人LoveR

描述----------Description----------

Generic function for the computation of asymptotic risks. This function is rarely called directly. It is used by  other functions.
通用函数计算的渐近风险。很少直接调用此函数。它被用来由其它函数。


用法----------Usage----------


getAsRisk(risk, L2deriv, neighbor, ...)

## S4 method for signature 'asMSE,UnivariateDistribution,Neighborhood'
getAsRisk(risk, L2deriv, neighbor, clip, cent, stand, trafo)

## S4 method for signature 'asMSE,EuclRandVariable,Neighborhood'
getAsRisk(risk, L2deriv, neighbor, clip, cent, stand, trafo)

## S4 method for signature 'asBias,UnivariateDistribution,ContNeighborhood'
getAsRisk(risk, L2deriv, neighbor, trafo)

## S4 method for signature 'asBias,UnivariateDistribution,TotalVarNeighborhood'
getAsRisk(risk, L2deriv, neighbor, trafo)

## S4 method for signature 'asBias,RealRandVariable,ContNeighborhood'
getAsRisk(risk, L2deriv, neighbor, Distr, L2derivDistrSymm, trafo,
             z.start, A.start, maxiter, tol)

## S4 method for signature 'asCov,UnivariateDistribution,ContNeighborhood'
getAsRisk(risk, L2deriv, neighbor, clip, cent, stand)

## S4 method for signature 'asCov,UnivariateDistribution,TotalVarNeighborhood'
getAsRisk(risk, L2deriv, neighbor, clip, cent, stand)

## S4 method for signature 'asCov,RealRandVariable,ContNeighborhood'
getAsRisk(risk, L2deriv, neighbor, Distr, clip, cent, stand)

## S4 method for signature 'trAsCov,UnivariateDistribution,UncondNeighborhood'
getAsRisk(risk, L2deriv, neighbor, clip, cent, stand)

## S4 method for signature 'trAsCov,RealRandVariable,ContNeighborhood'
getAsRisk(risk, L2deriv, neighbor, Distr, clip, cent, stand)

## S4 method for signature 'asUnOvShoot,UnivariateDistribution,UncondNeighborhood'
getAsRisk(risk, L2deriv, neighbor, clip, cent, stand, trafo)



参数----------Arguments----------

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


参数：L2deriv
L2-derivative of some L2-differentiable family of probability distributions.
L2-衍生的概率分布的一些L2-微的家庭。


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.
最优的中心不变。


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：Distr
object of class "Distribution".
对象类"Distribution"。


参数：L2derivDistrSymm
object of class "DistrSymmList".
对象类"DistrSymmList"。


参数：z.start
initial value for the centering constant.
定心常数的初始值。


参数：A.start
initial value for the standardizing matrix.
标准化矩阵的初始值。


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）。


值----------Value----------

The asymptotic risk is computed.
的渐近计算风险。


方法----------Methods----------

  


risk = "asMSE", L2deriv = "UnivariateDistribution", neighbor = "Neighborhood": computes asymptotic mean square error in methods for function getInfRobIC.
风险=的“asMSE”，L2deriv =“UnivariateDistribution”，邻居=“邻居”：计算渐近均方误差函数getInfRobIC方法。




risk = "asMSE", L2deriv = "EuclRandVariable", neighbor = "Neighborhood":  computes asymptotic mean square error in methods for function getInfRobIC.
风险=“asMSE”，L2deriv =“EuclRandVariable的”邻居“邻居”：计算函数getInfRobIC渐近均方误差的方法。




risk = "asBias", L2deriv = "UnivariateDistribution", neighbor = "ContNeighborhood":  computes standardized asymptotic bias in methods for function getInfRobIC.
风险=的“asBias”，L2deriv =“UnivariateDistribution”，邻居=“ContNeighborhood”：计算标准化的渐近偏差的方法函数getInfRobIC。




risk = "asBias", L2deriv = "UnivariateDistribution", neighbor = "TotalVarNeighborhood":  computes standardized asymptotic bias in methods for function getInfRobIC.
风险=的“asBias”，L2deriv =“UnivariateDistribution”，邻居=“TotalVarNeighborhood”：计算标准化的渐近偏差的方法函数getInfRobIC。




risk = "asBias", L2deriv = "RealRandVariable", neighbor = "ContNeighborhood":  computes standardized asymptotic bias in methods for function getInfRobIC.
风险=“asBias”，L2deriv =“RealRandVariable的”邻居“ContNeighborhood”：计算标准化的渐近偏差的方法函数getInfRobIC。




risk = "asCov", L2deriv = "UnivariateDistribution", neighbor = "ContNeighborhood":  computes asymptotic covariance in methods for function getInfRobIC.
的风险=“asCov”的，L2deriv =“UnivariateDistribution”，邻居=“ContNeighborhood”：计算渐近协方差的方法函数getInfRobIC。




risk = "asCov", L2deriv = "UnivariateDistribution", neighbor = "TotalVarNeighborhood":  computes asymptotic covariance in methods for function getInfRobIC.
的风险=“asCov”的，L2deriv =“UnivariateDistribution”，邻居=“TotalVarNeighborhood”：计算渐近协方差的方法函数getInfRobIC。




risk = "asCov", L2deriv = "RealRandVariable", neighbor = "ContNeighborhood":  computes asymptotic covariance in methods for function getInfRobIC.
风险=“asCov”，L2deriv =“RealRandVariable的”邻居“ContNeighborhood”：计算渐近协方差的方法函数getInfRobIC。




risk = "trAsCov", L2deriv = "UnivariateDistribution", neighbor = "UncondNeighborhood":  computes trace of asymptotic covariance in methods  for function getInfRobIC.
风险=的“trAsCov”，L2deriv的“UnivariateDistribution”，邻居=的“UncondNeighborhood”的渐近协方差函数getInfRobIC方法：计算跟踪。




risk = "trAsCov", L2deriv = "RealRandVariable", neighbor = "ContNeighborhood":  computes trace of asymptotic covariance in methods for  function getInfRobIC.
的风险=“trAsCov”，L2deriv =“RealRandVariable的”，邻居=“ContNeighborhood：计算跟踪的渐近协方差的方法函数getInfRobIC。




risk = "asUnOvShoot", L2deriv = "UnivariateDistribution", neighbor = "UncondNeighborhood":  computes asymptotic under-/overshoot risk in methods for  function getInfRobIC.   
风险=的“asUnOvShoot”，L2deriv的“UnivariateDistribution”邻居“UncondNeighborhood”：函数getInfRobIC方法计算的渐近under-/overshoot风险的。


（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. (1994) Robust Asymptotic Statistics. New York: Springer.
Ruckdeschel, P. and Rieder, H. (2004) Optimal Influence Curves for General Loss Functions. Statistics &amp; Decisions (submitted).
Kohl, M. (2005) Numerical Contributions to the Asymptotic Theory of Robustness.  Bayreuth: Dissertation.

参见----------See Also----------

asRisk-class
asRisk-class

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


注：
注1：为了方便大家学习，本文档为生物统计家园网机器人LoveR翻译而成，仅供个人R语言学习参考使用，生物统计家园保留版权。
注2：由于是机器人自动翻译，难免有不准确之处，使用时仔细对照中、英文内容进行反复理解，可以帮助R语言的学习。
注3：如遇到不准确之处，请在本贴的后面进行回帖，我们会逐渐进行修订。