falk(smoothtail)
falk()所属R语言包:smoothtail
Compute original and smoothed version of Falk's estimator
Falk的估计计算和平滑的版本
译者:生物统计家园网 机器人LoveR
描述----------Description----------
Given an ordered sample of either exceedances or upper order statistics which is to be modeled using a GPD, this function provides Falk's estimator of the shape parameter γ \in [-1,0]. Precisely,
给定一个有序样品的超标或上订单统计数据进行建模使用GPD,此功能提供Falk的估计,形状参数γ \in [-1,0]。准确地说,
for $H$ either the empirical or the distribution function based on the log–concave density estimator. Note that for any k, \hat γ_{\rm{Falk}} : R^n \to (-∞, 0). If \hat γ_{\rm{Falk}} \not \in [-1,0), then it is likely that the log-concavity assumption is violated.
$ H $或经验或对数凹的密度估计的分布函数的基础上。请注意,任何k,\hat γ_{\rm{Falk}} : R^n \to (-∞, 0)。如果\hat γ_{\rm{Falk}} \not \in [-1,0),那么它很可能,log凹假设的被侵犯。
用法----------Usage----------
falk(est, ks = NA)
参数----------Arguments----------
参数:est
Log-concave density estimate based on the sample as output by logConDens (a dlc object).
登录凹根据样本的密度估计为输出logConDens(dlc对象)。
参数:ks
Indices k at which Falk's estimate should be computed. If set to NA defaults to 3, …, n-1. </table>
指数kFalk的估计应计算。如果设置为NA默认为3, …, n-1的。 </ TABLE>
值----------Value----------
n x 3 matrix with columns: indices k, Falk's estimator based on the log-concave density estimate, and the ordinary Falk's estimator based on the order statistics.
NX 3的矩阵列指数k,Falk的估计,对数凹的密度估计,和普通Falk的次序统计量的基础上估计的基础上。
(作者)----------Author(s)----------
Kaspar Rufibach (maintainer), <a href="mailto:kaspar.rufibach@gmail.com">kaspar.rufibach@gmail.com</a>, <br> <a href="http://www.kasparrufibach.ch">http://www.kasparrufibach.ch</a>
Samuel Mueller, <a href="mailto:s.mueller@maths.usyd.edu.au">s.mueller@maths.usyd.edu.au</a>, <br> <a href="http://www.maths.usyd.edu.au/ut/people?who=S_Mueller">http://www.maths.usyd.edu.au/ut/people?who=S_Mueller</a>
Kaspar Rufibach acknowledges support by the Swiss National Science Foundation SNF, <a href="http://www.snf.ch">http://www.snf.ch</a>
参考文献----------References----------
Smooth tail index estimation. J. Stat. Comput. Simul., 79, 1155–1167.
Some best parameter estimates for distributions with finite endpoint. Statistics, 27, 115–125.
参见----------See Also----------
Other approaches to estimate γ based on the fact that the density is log–concave, thus
其他方法来估计γ,从而根据一个事实,即密度为log凹
实例----------Examples----------
# generate ordered random sample from GPD[形成有序的随机抽样GPD]
set.seed(1977)
n <- 20
gam <- -0.75
x <- rgpd(n, gam)
## generate dlc object[#生成DLC对象]
est <- logConDens(x, smoothed = FALSE, print = FALSE, gam = NULL, xs = NULL)
# compute tail index estimator[计算尾部指数估计]
falk(est)
转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。
注:
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