Conditional adaptive Bayesian spectral analysis of replicated multivariate time series.
Conditional adaptive Bayesian spectral analysis of replicated multivariate time series.
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DOI：
10.1002/sim.8884
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发表时间：
2021-04-15
影响因子：
2
通讯作者：
Long Y
中科院分区：
文献类型：
--
作者：
Li Z;Bruce SA;Wutzke CJ;Long Y
本文介绍了一种分析多元时间序列协变量与功率谱之间相关性的灵活的非参数方法，我们称之为多变量条件自适应贝叶斯功率谱分析。该方法自适应地将具有相似协变量值的时间序列收集到未知数目的组中，并通过惩罚样条法非参数估计特定组的功率谱。利用马尔可夫链蒙特卡罗技术，建立了一个完全贝叶斯框架，其中群的数量和定义群的协变量划分是随机的且符合。多CABS通过对协变量分区分布进行平均，提供了对具有平稳和突变动态的多变量时间序列功率谱的准确估计和推断。在仿真研究中，对该方法与现有方法的性能进行了比较。该方法被用来分析帕金森氏症患者站立时对跌倒的恐惧与站立时姿势控制压力中心轨迹的功率谱之间的关联。
This article introduces a flexible nonparametric approach for analyzing the association between covariates and power spectra of multivariate time series observed across multiple subjects, which we refer to as multivariate conditional adaptive Bayesian power spectrum analysis (MultiCABS). The proposed procedure adaptively collects time series with similar covariate values into an unknown number of groups and nonparametrically estimates group-specific power spectra through penalized splines. A fully Bayesian framework is developed in which the number of groups and the covariate partition defining the groups are random and fit using Markov chain Monte Carlo techniques. MultiCABS offers accurate estimation and inference on power spectra of multivariate time series with both smooth and abrupt dynamics across covariate by averaging over the distribution of covariate partitions. Performance of the proposed method compared with existing methods is evaluated in simulation studies. The proposed methodology is used to analyze the association between fear of falling and power spectra of center-of-pressure trajectories of postural control while standing in people with Parkinson’s disease.
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影响因子：
3
作者：
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通讯作者：
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作者：
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DOI：
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发表时间：
2019-01-02
影响因子：
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通讯作者：
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DOI：
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发表时间：
2006-10-01
影响因子：
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通讯作者：
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