tsbootstrap(tseries)
tsbootstrap()所属R语言包：tseries
Bootstrap for General Stationary Data
引导一般固定数据
译者：生物统计家园网 机器人LoveR
描述----------Description----------
tsbootstrap generates bootstrap samples for general stationary data and computes the bootstrap estimate of standard error and bias if a statistic is given.
tsbootstrap生成的bootstrap样本一般固定数据的统计和计算的引导估计标准误差和偏差。
用法----------Usage----------
tsbootstrap(x, nb = 1, statistic = NULL, m = 1, b = NULL,
type = c("stationary","block"), ...)
参数----------Arguments----------
参数：x
a numeric vector or time series giving the original data.
一个数值向量或时间序列给出的原始数据。
参数：nb
the number of bootstrap series to compute.
引导系列数计算。
参数：statistic
a function which when applied to a time series returns a vector containing the statistic(s) of interest.
其中，当施加到一个时间序列的函数返回一个向量，包含的统计量（s）的权益。
参数：m
the length of the basic blocks in the block of blocks bootstrap.
的基本程序块中的块的块的自举的长度。
参数：b
if type is "stationary", then b is the mean block length. If type is "block", then b is the fixed block length.
如果type是"stationary"，那么b是在平均块长度。 type如果是"block"，那么b是固定块长度。
参数：type
the type of bootstrap to generate the simulated time series. The possible input values are "stationary" (stationary bootstrap with mean block length b) and "block" (blockwise bootstrap with block length b). Default to "stationary".
的类型的自举生成模拟的时间序列。可能的输入值是"stationary"（固定的引导与平均块长度b）和"block"（列块的引导块长度b）。默认以"stationary"。
参数：...
additional arguments for statistic which are passed unchanged each time statistic is called.
额外的参数statistic传递不变的每次statistic被称为。
Details
详细信息----------Details----------
If type is "stationary", then the stationary bootstrap scheme with mean block length b according to Politis and Romano (1994) is computed. For type equals "block", the blockwise bootstrap with block length b according to Kuensch (1989) is used.
type如果是"stationary"，然后固定引导计划，平均块长度b根据“政治和罗马（1994年）计算。对于type等于"block"，列块的引导块长度b根据Kuensch（1989）使用。
If m > 1, then the block of blocks bootstrap is computed (see Kuensch, 1989). The basic sampling scheme is the same as for the case m = 1, except that the bootstrap is applied to a series y containing blocks of length m, where each block of y is defined as y[t] = (x[t], …, x[t-m+1]). Therefore, for the block of blocks bootstrap the first argument of statistic is given by a n x m matrix yb, where each row of yb contains one bootstrapped basic block observation y[t] (n is the number of observations in x).
如果m > 1，然后计算的块的块的自举（见Kuensch，1989）。基本采样方案m = 1，除了自举施加一系列y包含块的长度m，其中每个块的y的情况下是相同被定义为y[t] = (x[t], …, x[t-m+1])。因此，对于块引导块中的第一个参数statistic的n x m矩阵yb，其中的每一行yb包含一个自举的基本块观察y[t]（n是x）的若干意见。
Note, that for statistics which are functions of the empirical m-dimensional marginal (m > 1) only this procedure yields asymptotically valid bootstrap estimates. The case m = 1 may only be used for symmetric statistics (i.e., for statistics which are invariant under permutations of x). tsboot does not implement the block of blocks bootstrap, and, therefore, the first example in tsboot yields inconsistent estimates.
注意，统计的经验m维边际的功能（m > 1）只此过程中产生的bootstrap估计渐近有效的。的情况下m = 1只可用于对称的统计数据（即，这是不变的排列x）的统计。 tsboot不执行块的块引导，因此，在第一个例子中tsboot产生不一致的估计。
For consistency, the (mean) block length b should grow with n at an appropriate rate. If b is not given, then a default growth rate of const * n^(1/3) is used. This rate is "optimal" under certain conditions (see the references for more details). However, in general the growth rate depends on the specific properties of the data generation process. A default value for const has been determined by a Monte Carlo simulation using a Gaussian AR(1) process (AR(1)-parameter of 0.5, 500 observations). const has been chosen such that the mean square error for the bootstrap estimate of the variance of the empirical mean is minimized.
为了保持一致性，（平均）块长度b增长，n在适当的速度。如果b没有给出，那么默认的增长速度const * n^(1/3)使用。这个比率是“最优”在一定条件下（有关详细信息，请参阅参考资料）。然而，在一般的生长速率取决于数据生成过程的特定属性。的默认值const已确定由蒙地卡罗模拟使用高斯AR（1）过程（AR（1）参数为0.5，500观察）。 const已被选定，使得经验平均值的方差的bootstrap估计的均方误差最小化。
Note, that the computationally intensive parts are fully implemented in C which makes tsbootstrap about 10 to 30 times faster than tsboot.
注意，计算密集的部位得到充分的实施C，使tsbootstrap约10至30倍的速度比tsboot。
Missing values are not allowed.
遗漏值是不允许的。
There is a special print method for objects of class "resample.statistic" which by default uses max(3, getOption("digits") - 3) digits to format real numbers.
有一个特殊的类的对象的print方法，"resample.statistic"默认情况下，使用max(3, getOption("digits") - 3)数字的格式化实数。
值----------Value----------
If statistic is NULL, then it returns a matrix or time series with nb columns and length(x) rows containing the bootstrap data. Each column contains one bootstrap sample.
如果statistic是NULL，然后返回一个矩阵或nb列和length(x)行包含引导数据的时间序列。一个列包含一个引导样品。
If statistic is given, then a list of class "resample.statistic" with the following elements is returned:
如果statistic给定的，那么类"resample.statistic"的列表包含下列元素，则返回：
参数：statistic
the results of applying statistic to each of the simulated time series.
施加statistic每个模拟时间系列的结果。
参数：orig.statistic
the results of applying statistic to the original series.
应用statistic原始的系列。
参数：bias
the bootstrap estimate of the bias of statistic.
的自举估计的偏压statistic。
参数：se
the bootstrap estimate of the standard error of statistic.
自举估计的标准误差statistic。
参数：call
the original call of tsbootstrap.
原来的呼叫tsbootstrap。
（作者）----------Author(s)----------
A. Trapletti
参考文献----------References----------
The Jackknife and the Bootstrap for General Stationary Observations. The Annals of Statistics 17, 1217–1241.
The Stationary Bootstrap. Journal of the American Statistical Association 89, 1303–1313.
参见----------See Also----------
sample, surrogate, tsboot
sample，surrogate，tsboot
实例----------Examples----------
n <- 500 # Generate AR(1) process[生成AR（1）过程]
a <- 0.6
e <- rnorm(n+100)
x <- double(n+100)
x[1] <- rnorm(1)
for(i in 2 n+100)) {
x[i] <- a * x[i-1] + e[i]
}
x <- ts(x[-(1:100)])
tsbootstrap(x, nb=500, statistic=mean)
# Asymptotic formula for the std. error of the mean[渐近公式的性病。平均误差]
sqrt(1/(n*(1-a)^2))
acflag1 <- function(x)
{
xo <- c(x[,1], x[1,2])
xm <- mean(xo)
return(mean((x[,1]-xm)*(x[,2]-xm))/mean((xo-xm)^2))
}
tsbootstrap(x, nb=500, statistic=acflag1, m=2)
# Asymptotic formula for the std. error of the acf at lag one[渐近公式的性病。滞后1 acf的误差]
sqrt(((1+a^2)-2*a^2)/n)
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。
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发表于 2012-10-1 12:41:00
