Adaptive Bayesian Time-Frequency Analysis of Multivariate Time Series
Adaptive Bayesian Time-Frequency Analysis of Multivariate Time Series
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DOI：
10.1080/01621459.2017.1415908
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发表时间：
2019-01-02
影响因子：
3.7
通讯作者：
Krafty, Robert T.
中科院分区：
文献类型：
--
作者：
Li, Zeda;Krafty, Robert T.
本文介绍了一种非参数的多元时变功率谱分析方法。该过程自适应地将时间序列划分为未知数量的近似稳定的段，其中一些谱分量可以在段之间保持不变，从而允许分量随时间不同地演变。段内的局部光谱通过修改的Cholesky分量的基于Whittle似然的惩罚样条模型拟合，该模型提供了灵活的非参数估计，保留了谱矩阵的正定结构。该方法是在贝叶斯框架中制定的，其中分区的数量和位置是随机的，并且依赖于可逆跳马尔可夫链和哈密顿蒙特卡罗方法，可以适应未知数量的段和参数。通过对分区的分布进行平均，该方法可以近似谱矩阵中的突然变化和缓慢变化。经验的性能进行评估，在模拟研究和说明通过分析睡眠期间的脑电图和厄尔尼诺南方涛动。本文的补充材料可在网上查阅。
This article introduces a nonparametric approach to multivariate time-varying power spectrum analysis. The procedure adaptively partitions a time series into an unknown number of approximately stationary segments, where some spectral components may remain unchanged across segments, allowing components to evolve differently over time. Local spectra within segments are fit through Whittle likelihood-based penalized spline models of modified Cholesky components, which provide flexible nonparametric estimates that preserve positive definite structures of spectral matrices. The approach is formulated in a Bayesian framework, in which the number and location of partitions are random, and relies on reversible jump Markov chain and Hamiltonian Monte Carlo methods that can adapt to the unknown number of segments and parameters. By averaging over the distribution of partitions, the approach can approximate both abrupt and slowly varying changes in spectral matrices. Empirical performance is evaluated in simulation studies and illustrated through analyses of electroencephalography during sleep and of the El Nino-Southern Oscillation. Supplementary materials for this article are available online.