leastFavorableRadius(ROptEst)
leastFavorableRadius()所属R语言包：ROptEst
Generic Function for the Computation of Least Favorable Radii
最有利的半径计算的通用功能
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Generic function for the computation of least favorable radii.
最不利的半径计算的通用功能。
用法----------Usage----------
leastFavorableRadius(L2Fam, neighbor, risk, ...)
## S4 method for signature 'L2ParamFamily,UncondNeighborhood,asGRisk'
leastFavorableRadius(
L2Fam, neighbor, risk, rho, upRad = 1,
z.start = NULL, A.start = NULL, upper = 100,
OptOrIter = "iterate", maxiter = 100,
tol = .Machine$double.eps^0.4, warn = FALSE, verbose = NULL)
参数----------Arguments----------
参数：L2Fam
L2-differentiable family of probability measures.
L2-微家庭的概率措施。
参数：neighbor
object of class "Neighborhood".
对象类"Neighborhood"。
参数：risk
object of class "RiskType".
对象类"RiskType"。
参数：...
additional parameters
额外的参数
参数：upRad
the upper end point of the radius interval to be searched.
上端点的半径间隔进行搜索。
参数：rho
The considered radius interval is: [r*rho, r/rho] with 0 < rho < 1.
所考虑的半径间隔为：[r*rho, r/rho]0 < rho < 1。
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：upper
upper bound for the optimal clipping bound.
上界的最佳剪辑约束。
参数：OptOrIter
character; which method to be used for determining Lagrange multipliers A and a: if (partially) matched to "optimize", getLagrangeMultByOptim is used; otherwise: by default, or if matched to "iterate" or to "doubleiterate", getLagrangeMultByIter is used. More specifically, when using getLagrangeMultByIter, and if argument risk is of class "asGRisk", by default and if matched to "iterate" we use only one (inner) iteration, if matched to "doubleiterate" we use up to Maxiter (inner) iterations.
方法用于确定拉格朗日乘子的性格;A和a：如果（部分）匹配"optimize"，getLagrangeMultByOptim使用，否则默认情况下，或者如果相匹配的"iterate"或"doubleiterate"，getLagrangeMultByIter使用。更具体地，使用时：getLagrangeMultByIter，如果参数risk是类"asGRisk"，缺省情况下，和如果"iterate"匹配我们只使用一个（内）迭代，如果匹配"doubleiterate"的“我们使用Maxiter（内部）迭代。
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：warn
logical: print warnings.
逻辑：打印警告。
参数：verbose
logical: if TRUE, some messages are printed
逻辑：如果TRUE，一些消息都印
值----------Value----------
The least favorable radius and the corresponding inefficiency are computed.
至少有利的半径和相应的无效率计算。
方法----------Methods----------
L2Fam = "L2ParamFamily", neighbor = "UncondNeighborhood", risk = "asGRisk" computation of the least favorable radius.
L2Fam =“L2ParamFamily”，邻居=“UncondNeighborhood”风险=“asGRisk”的计算最不利的半径。
（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., Kohl, M. and Ruckdeschel, P. (2008) The Costs of not Knowing the Radius. Statistical Methods and Applications 17(1) 13-40.
Rieder, H., Kohl, M. and Ruckdeschel, P. (2001) The Costs of not Knowing the Radius. Submitted. Appeared as discussion paper Nr. 81. SFB 373 (Quantification and Simulation of Economic Processes), Humboldt University, Berlin; also available under www.uni-bayreuth.de/departments/math/org/mathe7/RIEDER/pubs/RR.pdf
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----------
radiusMinimaxIC
radiusMinimaxIC
实例----------Examples----------
N <- NormLocationFamily(mean=0, sd=1)
leastFavorableRadius(L2Fam=N, neighbor=ContNeighborhood(),
risk=asMSE(), rho=0.5)
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。
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发表于 2012-9-27 23:17:28
