Nonparametric Spectral Analysis of Multivariate Time Series
Nonparametric Spectral Analysis of Multivariate Time Series
复制标题
多元时间序列的非参数谱分析
DOI：
10.1146/annurev-statistics-031219-041138
复制
发表时间：
2020
影响因子：
7.9
通讯作者：
R. Sachs
中科院分区：
文献类型：
--
作者：
R. Sachs
多元时间序列的谱分析在过去的50年里一直是方法论和应用统计学的一个活跃领域。由于快速傅里叶变换算法的成功，在频域中对序列自相关和互相关的分析帮助我们理解了许多序列相关数据中的动态，而不必开发复杂的参数模型。在这项工作中，我们对最近的多变量时间序列谱分析的非参数方法进行了非详尽的回顾，重点是基于模型的方法。我们试图对标准和不太标准的情况（如非平稳、复制或高维时间序列）的各种补充方法给出见解，讨论估计方面（如平滑频率），并包括一些来自生命科学应用的例子（如大脑数据）。
Spectral analysis of multivariate time series has been an active field of methodological and applied statistics for the past 50 years. Since the success of the fast Fourier transform algorithm, the analysis of serial auto- and cross-correlation in the frequency domain has helped us to understand the dynamics in many serially correlated data without necessarily needing to develop complex parametric models. In this work, we give a nonexhaustive review of the mostly recent nonparametric methods of spectral analysis of multivariate time series, with an emphasis on model-based approaches. We try to give insights into a variety of complimentary approaches for standard and less standard situations (such as nonstationary, replicated, or high-dimensional time series), discuss estimation aspects (such as smoothing over frequency), and include some examples stemming from life science applications (such as brain data).
影响因子：
1.5
作者：
R. Dahlhaus;S. Richter;W. Wu
通讯作者：
R. Dahlhaus;S. Richter;W. Wu
影响因子：
5.4
作者：
Park, Timothy;Eckley, Idris A.;Ombao, Hernando C.
通讯作者：
Ombao, Hernando C.