rlsOptIC.AL(RobLox)
rlsOptIC.AL()所属R语言包：RobLox
Computation of the optimally robust IC for AL estimators
AL估计计算的最佳强大的IC
译者：生物统计家园网 机器人LoveR
描述----------Description----------
The function rlsOptIC.AL computes the optimally robust IC for AL estimators in case of normal location with unknown scale and (convex) contamination neighborhoods. The definition of these estimators can be found in Section 8.2 of Kohl (2005).
的功能rlsOptIC.AL计算最优强大的IC AL估计的情况下，正常的位置，未知的规模（凸）污染居民区。这些估计的定义，可以发现在科尔（2005年）的第8.2节。
用法----------Usage----------
rlsOptIC.AL(r, mean = 0, sd = 1, A.loc.start = 1, a.sc.start = 0,
A.sc.start = 0.5, bUp = 1000, delta = 1e-6, itmax = 100,
check = FALSE, computeIC = TRUE)
参数----------Arguments----------
参数：r
non-negative real: neighborhood radius.
非负实：邻域半径。
参数：mean
specified mean.
指定的意思。
参数：sd
specified standard deviation.
指定的标准偏差。
参数：A.loc.start
positive real: starting value for the standardizing constant of the location part.
正实：起始值的位置部分的标准化常数。
参数：a.sc.start
real: starting value for centering constant of the scale part.
实际：起始值为中心的规模不变。
参数：A.sc.start
positive real: starting value for the standardizing constant of the scale part.
正实：起始值为标准化的规模不变。
参数：bUp
positive real: the upper end point of the interval to be searched for the clipping bound b.
正实的上端点的时间间隔要搜索的裁剪边界B。
参数：delta
the desired accuracy (convergence tolerance).
所需的精度（收敛宽容）。
参数：itmax
the maximum number of iterations.
最大迭代次数。
参数：check
logical: should constraints be checked.
逻辑：应约束条件进行检查。
参数：computeIC
logical: should IC be computed. See details below.
逻辑：IC计算。详见下文。
Details
详细信息----------Details----------
The Lagrange multipliers contained in the expression of the optimally robust IC can be accessed via the accessor functions cent, clip and stand. If 'computeIC' is 'FALSE' only the Lagrange multipliers 'A', 'a', and 'b' contained in the optimally robust IC are computed.
的访问函数的拉格朗日乘子的最佳强大的IC中的表达可通过cent，clip和stand。 ，如果“computeIC”是FALSE的拉格朗日乘子A，A，和B的最佳强大的IC中包含的计算。
值----------Value----------
If 'computeIC' is 'TRUE' an object of class "ContIC" is returned, otherwise a list of Lagrange multipliers <table summary="R valueblock"> <tr valign="top"><td>A</td> <td> standardizing matrix </td></tr> <tr valign="top"><td>a</td> <td> centering vector </td></tr> <tr valign="top"><td>b</td> <td> optimal clipping bound </td></tr> </table>
如果computeIC是TRUE的对象类"ContIC"，否则将返回一个列表拉格朗日乘子表summary="R valueblock"> <tr valign="top"> <TD>A </ TD> <TD>标准化矩阵</ TD> </ TR> <tr valign="top"> <TD>a </ TD> <TD>定心向量</ TD> </ TR > <tr valign="top"> <TD> b </ TD> <TD>最佳裁剪绑定</ TD> </ TR> </ TABLE>
（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.
Kohl, M. (2005) Numerical Contributions to the Asymptotic Theory of Robustness. Bayreuth: Dissertation.
参见----------See Also----------
ContIC-class, roblox
ContIC-class，roblox
实例----------Examples----------
IC1 <- rlsOptIC.AL(r = 0.1, check = TRUE)
distrExOptions("ErelativeTolerance" = 1e-12)
checkIC(IC1)
distrExOptions("ErelativeTolerance" = .Machine$double.eps^0.25) # default[默认]
Risks(IC1)
cent(IC1)
clip(IC1)
stand(IC1)
plot(IC1)
infoPlot(IC1)
## k-step estimation[＃k步估计]
## better use function roblox (see ?roblox)[＃更好的的使用功能ROBLOX（看到了吗？ROBLOX）]
## 1. data: random sample[＃1。数据：随机抽样]
ind <- rbinom(100, size=1, prob=0.05)
x <- rnorm(100, mean=0, sd=(1-ind) + ind*9)
mean(x)
sd(x)
median(x)
mad(x)
## 2. Kolmogorov(-Smirnov) minimum distance estimator (default)[＃2。柯尔莫哥洛夫 - 斯米尔诺夫的最小距离估计（默认）]
## -> we use it as initial estimate for one-step construction[“＃ - >我们使用它作为初步估计，单步建设]
(est0 <- MDEstimator(x, ParamFamily = NormLocationScaleFamily()))
## 3.1 one-step estimation: radius known[＃3.1单步估算：半径称为]
IC1 <- rlsOptIC.AL(r = 0.5, mean = estimate(est0)[1], sd = estimate(est0)[2])
(est1 <- oneStepEstimator(x, IC1, est0))
## 3.2 k-step estimation: radius known[＃3.2 k步估计半径称为：]
## Choose k = 3[＃选择K = 3]
(est2 <- kStepEstimator(x, IC1, est0, steps = 3L))
## 4.1 one-step estimation: radius unknown[4.1单步估算：半径未知]
## take least favorable radius r = 0.579[＃最不利的半径R = 0.579]
## cf. Table 8.1 in Kohl(2005)[＃CF。表8.1科尔（2005）]
IC2 <- rlsOptIC.AL(r = 0.579, mean = estimate(est0)[1], sd = estimate(est0)[2])
(est3 <- oneStepEstimator(x, IC2, est0))
## 4.2 k-step estimation: radius unknown[＃4.2 k步估计：半径未知]
## take least favorable radius r = 0.579[＃最不利的半径R = 0.579]
## cf. Table 8.1 in Kohl(2005)[＃CF。表8.1科尔（2005）]
## choose k = 3[＃选择K = 3]
(est4 <- kStepEstimator(x, IC2, est0, steps = 3L))
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注：
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发表于 2012-9-27 21:06:45
