getFiRiskRegTS(ROptRegTS)
getFiRiskRegTS()所属R语言包:ROptRegTS
Generic Function for Computation of Finite-Sample Risks
通用函数计算的有限样本的风险
译者:生物统计家园网 机器人LoveR
描述----------Description----------
Generic function for the computation of finite-sample risks in regression-type models. This function is rarely called directly. It is used by other functions.
通用函数计算有限样本回归模型的风险。很少直接调用此函数。它被用来由其它函数。
用法----------Usage----------
getFiRiskRegTS(risk, ErrorDistr, Regressor, neighbor, ...)
## S4 method for signature 'fiUnOvShoot,Norm,UnivariateDistribution,ContNeighborhood'
getFiRiskRegTS(risk,
ErrorDistr, Regressor, neighbor, clip, stand, sampleSize, Algo, cont)
## S4 method for signature 'fiUnOvShoot,Norm,UnivariateDistribution,TotalVarNeighborhood'
getFiRiskRegTS(risk,
ErrorDistr, Regressor, neighbor, clip, stand, sampleSize, Algo, cont)
## S4 method for signature 'fiUnOvShoot,Norm,UnivariateDistribution,CondContNeighborhood'
getFiRiskRegTS(risk,
ErrorDistr, Regressor, neighbor, clip, stand, sampleSize, cont)
## S4 method for signature 'fiUnOvShoot,Norm,UnivariateDistribution,CondTotalVarNeighborhood'
getFiRiskRegTS(risk,
ErrorDistr, Regressor, neighbor, clip, stand, sampleSize, cont)
参数----------Arguments----------
参数:risk
object of class "RiskType".
对象类"RiskType"。
参数:ErrorDistr
error distribution
误差分布
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/function.
最佳削波/函数绑定。
参数:stand
standardizing matrix.
规范矩阵。
参数:sampleSize
integer: sample size.
整数:样本量。
参数:Algo
"A" or "B".
“A”或“B”。
参数:cont
"left" or "right".
“左”或“右”。
Details
详细信息----------Details----------
The computation of the finite-sample under-/overshoot risk is based on FFT. For more details we refer to Subsections 12.1.3 and 12.2.3 of Kohl (2005).
基于FFT计算的有限样本under-/overshoot的风险。有关详细信息,请参阅第12.1.3和12.2.3科尔(2005年)。
值----------Value----------
The finite-sample risk is computed.
有限样本风险计算。
方法----------Methods----------
risk = "fiUnOvShoot", ErrorDistr = "Norm", Regressor = "UnivariateDistribution", neighbor = "ContNeighborhood" computes finite-sample under-/overshoot risk in methods for function 'getFixRobRegTypeIC'.
危险=“fiUnOvShoot”,ErrorDistr =“规范”,REGRESSOR =“UnivariateDistribution”,邻居=“ContNeighborhood”的方法功能的getFixRobRegTypeIC“计算有限样本under-/overshoot的风险。
risk = "fiUnOvShoot", ErrorDistr = "Norm", Regressor = "UnivariateDistribution", neighbor = "TotalVarNeighborhood" computes finite-sample under-/overshoot risk in methods for function 'getFixRobRegTypeIC'.
危险=“fiUnOvShoot”,ErrorDistr =“规范”,REGRESSOR =“UnivariateDistribution”,邻居=“TotalVarNeighborhood”的方法功能的getFixRobRegTypeIC“计算有限样本under-/overshoot的风险。
risk = "fiUnOvShoot", ErrorDistr = "Norm", Regressor = "UnivariateDistribution", neighbor = "CondContNeighborhood" computes finite-sample under-/overshoot risk in methods for function 'getFixRobRegTypeIC'.
危险=“fiUnOvShoot”,ErrorDistr =“规范”,REGRESSOR =“UnivariateDistribution”,邻居=“CondContNeighborhood”的方法功能的getFixRobRegTypeIC“计算有限样本under-/overshoot的风险。
risk = "fiUnOvShoot", ErrorDistr = "Norm", Regressor = "UnivariateDistribution", neighbor = "CondTotalVarNeighborhood" computes finite-sample under-/overshoot risk in methods for function 'getFixRobRegTypeIC'.
危险=“fiUnOvShoot”,ErrorDistr =“规范”,REGRESSOR =“UnivariateDistribution”,邻居=“CondTotalVarNeighborhood”的方法功能的getFixRobRegTypeIC“计算有限样本under-/overshoot的风险。
(作者)----------Author(s)----------
Matthias Kohl <a href="mailto:Matthias.Kohl@stamats.de">Matthias.Kohl@stamats.de</a>
参考文献----------References----------
Huber, P.J. (1968) Robust Confidence Limits. Z. Wahrscheinlichkeitstheor. Verw. Geb. 10:269–278.
Rieder, H. (1989) A finite-sample minimax regression estimator. Statistics 20(2): 211–221.
Kohl, M. (2005) Numerical Contributions to the Asymptotic Theory of Robustness. Bayreuth: Dissertation.
参见----------See Also----------
fiRisk-class
fiRisk-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。
|