getInfClip(ROptEst)
getInfClip()所属R语言包：ROptEst
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 in case of infinitesimal robust models. This function is rarely called directly. It is used to compute optimally robust ICs.
通用功能的最佳剪辑约束的情况下的无穷可靠的模型计算。很少直接调用此函数。它被用来计算最佳鲁棒的IC。
用法----------Usage----------
getInfClip(clip, L2deriv, risk, neighbor, ...)
## S4 method for signature 'numeric,UnivariateDistribution,asMSE,ContNeighborhood'
getInfClip(clip, L2deriv,
risk, neighbor, biastype, cent, symm, trafo)
## S4 method for signature 'numeric,UnivariateDistribution,asMSE,TotalVarNeighborhood'
getInfClip(clip, L2deriv,
risk, neighbor, biastype, cent, symm, trafo)
## S4 method for signature 'numeric,UnivariateDistribution,asL1,ContNeighborhood'
getInfClip(clip, L2deriv,
risk, neighbor, biastype, cent, symm, trafo)
## S4 method for signature 'numeric,UnivariateDistribution,asL1,TotalVarNeighborhood'
getInfClip(clip, L2deriv,
risk, neighbor, biastype, cent, symm, trafo)
## S4 method for signature 'numeric,UnivariateDistribution,asL4,ContNeighborhood'
getInfClip(clip, L2deriv,
risk, neighbor, biastype, cent, symm, trafo)
## S4 method for signature 'numeric,UnivariateDistribution,asL4,TotalVarNeighborhood'
getInfClip(clip, L2deriv,
risk, neighbor, biastype, cent, symm, trafo)
## S4 method for signature 'numeric,EuclRandVariable,asMSE,UncondNeighborhood'
getInfClip(clip, L2deriv, risk,
neighbor, biastype, Distr, stand, cent, trafo)
## S4 method for signature 'numeric,UnivariateDistribution,asUnOvShoot,UncondNeighborhood'
getInfClip(clip, L2deriv,
risk, neighbor, biastype, cent, symm, trafo)
## S4 method for signature 'numeric,UnivariateDistribution,asSemivar,ContNeighborhood'
getInfClip(clip, L2deriv,
risk, neighbor, cent, symm, trafo)
参数----------Arguments----------
参数：clip
positive real: clipping bound
正实：剪切方向
参数：L2deriv
L2-derivative of some L2-differentiable family of probability measures.
L2-衍生的一些L2-微家庭的概率措施。
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：biastype
object of class "BiasType"
对象的类"BiasType"
参数：cent
optimal centering constant.
最优的中心不变。
参数：stand
standardizing matrix.
规范矩阵。
参数：Distr
object of class "Distribution".
对象类"Distribution"。
参数：symm
logical: indicating symmetry of L2deriv.
逻辑：表示对称的L2deriv。
参数：trafo
matrix: transformation of the parameter.
矩阵变换的参数。
值----------Value----------
The optimal clipping bound is computed.
绑定的最佳剪辑计算。
方法----------Methods----------
clip = "numeric", L2deriv = "UnivariateDistribution", risk = "asMSE", neighbor = "ContNeighborhood" optimal clipping bound for asymtotic mean square error.
夹=“数字”，L2deriv =“UnivariateDistribution”风险=“asMSE”的，邻居=的“ContNeighborhood”最佳裁剪渐进的均方误差的约束。
clip = "numeric", L2deriv = "UnivariateDistribution", risk = "asMSE", neighbor = "TotalVarNeighborhood" optimal clipping bound for asymtotic mean square error.
夹=“数字”，L2deriv =“UnivariateDistribution”风险=“asMSE”的，邻居=的“TotalVarNeighborhood”最佳裁剪渐进的均方误差的约束。
clip = "numeric", L2deriv = "EuclRandVariable", risk = "asMSE", neighbor = "UncondNeighborhood" optimal clipping bound for asymtotic mean square error.
夹=“数字”，L2deriv =“EuclRandVariable的”风险“asMSE”，邻居=的“UncondNeighborhood”最佳裁剪开往渐进的均方误差。
clip = "numeric", L2deriv = "UnivariateDistribution", risk = "asL1", neighbor = "ContNeighborhood" optimal clipping bound for asymtotic mean absolute error.
夹=“数字”，L2deriv =的“UnivariateDistribution”风险=“ASL1”邻居=“ContNeighborhood”的最佳剪裁开往渐进的平均绝对误差。
clip = "numeric", L2deriv = "UnivariateDistribution", risk = "asL1", neighbor = "TotalVarNeighborhood" optimal clipping bound for asymtotic mean absolute error.
夹=“数字”，L2deriv =的“UnivariateDistribution”风险=“ASL1”邻居=“TotalVarNeighborhood”的最佳剪裁开往渐进的平均绝对误差。
clip = "numeric", L2deriv = "UnivariateDistribution", risk = "asL4", neighbor = "ContNeighborhood" optimal clipping bound for asymtotic mean power 4 error.
夹=“数字”，L2deriv =“UnivariateDistribution”风险=“asL4”的，邻居=的“ContNeighborhood”最佳裁剪开往渐进的平均功率4个错误。
clip = "numeric", L2deriv = "UnivariateDistribution", risk = "asL4", neighbor = "TotalVarNeighborhood" optimal clipping bound for asymtotic mean power 4 error.
夹=“数字”，L2deriv =“UnivariateDistribution”风险=“asL4”的，邻居=的“TotalVarNeighborhood”最佳裁剪开往渐进的平均功率4个错误。
clip = "numeric", L2deriv = "UnivariateDistribution", risk = "asUnOvShoot", neighbor = "UncondNeighborhood" optimal clipping bound for asymtotic under-/overshoot risk.
夹=“数字”，L2deriv =“UnivariateDistribution”风险=“asUnOvShoot”的，邻居=“UncondNeighborhood”最佳裁剪为渐进的under-/overshoot风险的约束。
clip = "numeric", L2deriv = "UnivariateDistribution", risk = "asSemivar", neighbor = "ContNeighborhood" optimal clipping bound for asymtotic semivariance.
夹=“数字”，L2deriv =“UnivariateDistribution”风险=“asSemivar”的，邻居=的“ContNeighborhood”最佳裁剪渐进的变异函数的约束。
（作者）----------Author(s)----------
Matthias Kohl <a href="mailto:Matthias.Kohl@stamats.de">Matthias.Kohl@stamats.de</a>,
Peter Ruckdeschel <a href="mailto eter.Ruckdeschel@itwm.fraunhofer.de"> eter.Ruckdeschel@itwm.fraunhofer.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 22, 201-223.
Ruckdeschel, P. (2005) Optimally One-Sided Bounded Influence Curves. Mathematical Methods in Statistics 14(1), 105-131.
Kohl, M. (2005) Numerical Contributions to the Asymptotic Theory of Robustness. Bayreuth: Dissertation.
参见----------See Also----------
ContIC-class, TotalVarIC-class
ContIC-class，TotalVarIC-class
转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。
注：
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发表于 2012-9-27 23:15:38
