wavFDPBlock(wmtsa)
wavFDPBlock()所属R语言包：wmtsa
Block-dependent estimation of fractionally differenced (FD) model parameters
块相关的分数差分（FD）模型参数的估计
译者：生物统计家园网 机器人LoveR
描述----------Description----------
A discrete wavelet transform of the input series is used to calculate block-dependent estimates of the FD parameter, the variance of the FD parameter and the innovations variance. Both a maximum likelihood estimation (MLE) and weighted least squares estimation (WLSE) scheme are supported. If an MLE scheme is chosen, then the DWT is used for its ability to de-correlate long-memory processes. If a WLSE scheme is chosen, then the MODWT
的离散小波变换的输入序列被用来计算依赖于块的估计的FD参数的FD参数的方差和创新方差。最大似然估计（MLE）和加权最小二乘估计（WLSE）计划的支持。如果MLE方案被选中，然后DWT使用的能力相关的术语记忆的过程。如果一个WLSE计划选择，然后MODWT的
用法----------Usage----------
boundary=NULL, edof.mode=1,
estimator="wlse", delta.range=c(-10.0,10.0),
position=list(from=1,by=1,units=character()), units=character(),
参数----------Arguments----------
参数：x
a vector containing a uniformly-sampled real-valued time series.
一个向量，包含均匀采样的实值的时间序列。
参数：boundary
a character string representing the different methods by which boundary wavelet coefficients and scaling coefficients are handled in calculating the FD model parameters. The options for this argument are dependent upon the estimator argument.
一个字符串，代表了不同的方法处理边界的小波系数和尺度系数计算FD模型参数。这种说法的选项取决于estimator参数。
For the MLE case, the boundary options are:
对于MLE的情况下，boundary选项：
"stationary"Under a stationary FD process model, boundary wavelet and scaling coefficients are used in estimating the FD model parameters.
"stationary"在一个固定的FD过程模型，的边界小波变换和缩放系数被用于，FD模型参数估计。
"nonstationary"A stationary-nonstationary FD model assumes that the governing process may fall into the nonstationary regime and, accordingly, the boundary wavelet coefficients and scaling coefficients are excluded in estimating the FD model parameters.
"nonstationary"一个固定的非平稳FD模型假设的管治过程中可能会落入非平稳制度，因此，边界小波系数和尺度系数被排除在FD模型参数估计。
For the WLSE case, the boundary options are:
对于的WLSE情况下，boundary选项：
"biased"Boundary wavelet coefficients are included in the estimate.
"biased"边界小波系数包含在估计。
"unbiased"Boundary wavelet coefficients are excluded in the estimate.
"unbiased"的边界小波系数被排除在外的估计。
The scaling coefficients are (always) excluded in weighted least squares estimates of FD model parameters. Default: "unbiased".
缩放系数（总是）排除在外加权最小二乘，FD模型参数的估计。默认值："unbiased"。
参数：delta.range
a two-element vector containing the search range for the FD parameter. Typically, the range [-10,10] is suitable for all physical systems. Default: c(-10 10).
一个两个元素的向量，包含在搜索范围的FD参数。通常情况下，的范围内[-10,10]是适合所有的物理系统。默认值：c(-10 10)。
参数：documentation
a character string used to describe the input data. Default: character().
一个字符串用来描述输入data。默认值：character()。
参数：edof.mode
the mode by which the equivalent degrees of freedom are calculated. This argument is limited to 1,2, or 3 and is used only for the WLSE scheme. See wavEDOF for details. Default: 1.
的模式计算等效自由度。这个论点是有限的，以1,2或3，，仅用于WLSE计划。见wavEDOF的详细信息。默认值：1。
参数：estimator
a character string denoting the estimation method. Use "wlse" for a weighted least squares estimate and "mle" for a maximum likelihood estimate. Default: "wlse".
一个字符串表示的估算方法。使用"wlse"的加权最小二乘估计和"mle"的最大似然估计。默认值："wlse"。
参数：keep.series
a logical value. If TRUE, the original series is preserved in the output object. Default: FALSE.
一个逻辑值。如果TRUE，被保存在原系列的输出对象。默认值：FALSE。
参数：levels
a vector containing the decomposition levels. The levels may be given in any order but must be positive. Default: 1:J where J is the maximum wavelet decomposition level at which there exists at least one interior wavelet coefficient.
一个向量的分解水平。水平可能会以任何顺序，但必须是积极的。默认值：1:J其中J是最大的小波分解水平存在至少一个内部的小波系数。
参数：position
a list containing the arguments from, by and to which describe the position(s) of the input data. All position arguments need not be specified as missing members will be filled in by their default values. Default: list(from=1, by=1, units=character()).
list包含的参数from, by和to描述的位置（S）输入data。所有的位置参数需要被指定为缺少的成员将被填充，它们的默认值。默认值：list(from=1, by=1, units=character())。
参数：sdf
a vector containing a discretized approximation of the process spectral density function (SDF). The coefficients of this argument should correspond exactly with the normalized Fourier frequencies f=0, 1/P , 2/P, 3/P, ..., (M-1)/P, where P=2*(M-1) and M is the number of points in the SDF vector. For example, if the sdf vector contains five elements, the corresponding frequencies will be f=[0, 1/8, 1/4, 3/8, 1/2]. This argument is used only for the WLSE scheme when calculating EDOF mode 2 estimates. Default: NULL (EDOF mode 2 not used).
一个向量，包含过程谱密度函数（SDF）的一个离散近似。系数的这种说法完全一致的归一化的傅立叶频率f=0, 1/P , 2/P, 3/P, ..., (M-1)/P，其中P=2*(M-1)和M是在SDF矢量点的数量。例如，如果自卫队矢量包含五个元素，相应的频率将是f=[0, 1/8, 1/4, 3/8, 1/2]。这种说法是仅用于WLSE方案计算EDOF模式2时估计。默认值：NULL（EDOF模式2不使用）。
参数：title.data
a character string representing the name of the input data. Default: character().
一个字符串代表名称的输入data。默认值：character()。
参数：units
a string denoting the units of the time series. Default: character() (no units).
一个字符串，表示的时间序列的单位。默认值：character()（无单位）。
参数：wavelet
a character string denoting the filter type. See wavDaubechies for details. Default: "s8".
一个字符串，表示过滤器的类型。见wavDaubechies的详细信息。默认值："s8"。
Details
详细信息----------Details----------
When estimator="mle" and boundary="stationary", the levels vector is forced to take on values [1,2,...,J] where J is the maximum number of levels in a full DWT. This is done because (in this case) the scaling coefficient and all wavelet coefficients are used to form the FD model parameter estimates.
当estimator="mle"和boundary="stationary"，levels向量的值是被迫采取[1,2,...,J]其中J是在全DWT的最大数量的水平。这样做是因为（在这种情况下）的缩放系数和所有的小波系数被用于形成FD模型参数估计。
In using the WLSE scheme it is recommended that only the unbiased estimator be used since the confidence intervals for the biased estimator have not been sufficiently studied.
在使用WLSE计划，建议只的无偏估计的有偏估计的置信区间已被使用，因为没有得到充分的研究。
值----------Value----------
an object of class wavFDP.
对象类wavFDP。
参考文献----------References----------
D. B. Percival and A. T. Walden, Wavelet Methods for Time Series Analysis, Cambridge University Press, 2000, 340–92.
W. Constantine, D. B. Percival and P. G. Reinhall, Inertial Range Determination for Aerothermal Turbulence Using Fractionally Differenced Processes and Wavelets, Physical Review E, 2001, 64(036301), 12 pages.
参见----------See Also----------
实例----------Examples----------
## perform a block-averaged MLE of FD parameters [＃执行块平均极大似然估计的FD参数]
## for an FD(0.45, 1) realization over levels 1 [＃FD实现超过1级（0.45，1）]
## through 6 using a stationary-nonstationary [6＃通过使用一个固定的非平稳]
## FD model and Daubechies least asymmetric [＃FD模式和Daubechies小波至少不对称]
## 8-tap filters [＃8抽头的滤波器]
wavFDPBlock(fdp045, levels=1:6, wavelet="s8", est="mle", boundary="nonstationary")
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。
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发表于 2012-10-1 22:54:27
