重复多重时间序列的条件谱分析及其在夜间生理中的应用
Conditional Spectral Analysis of Replicated Multiple Time Series with Application to Nocturnal Physiology
摘要：
本文考虑了多个时间序列的功率谱和横截面结果之间的关联分析的问题时，从多个主题观察数据。这一激励性的应用来自睡眠医学，研究人员能够在睡眠期间非侵入性地记录生理时间序列信号。这些信号的频率模式，可以通过功率谱量化，包含有关生物过程的可解释的信息。睡眠研究中的一个重要问题是在时间序列信号的功率谱和临床特征之间建立联系;这些联系是理解睡眠影响健康的生物学途径的关键，并且可以通过治疗来改善健康。这样的分析是具有挑战性的，因为它们必须克服复杂的结构的功率谱从多个时间序列作为一个复杂的正定矩阵值函数。本文提出了一种新的方法，这种分析的基础上张量积样条模型的结果依赖功率谱的乔莱斯基分量。该方法灵活地将功率谱建模为频率和结果的非参数函数，同时保留几何约束。在一个完全贝叶斯的框架下，惠特尔似然为基础的马尔可夫链蒙特卡罗（MCMC）算法开发的自动模型拟合和进行推理的结果和光谱测量之间的关联。该方法用于分析老年人睡眠研究的数据，并揭示了压力和唤醒如何与一个人在床上花费的时间有关的新见解。
英文摘要：
This article considers the problem of analyzing associations between power spectra of multiple time series and cross-sectional outcomes when data are observed from multiple subjects. The motivating application comes from sleep medicine, where researchers are able to non-invasively record physiological time series signals during sleep. The frequency patterns of these signals, which can be quantified through the power spectrum, contain interpretable information about biological processes. An important problem in sleep research is drawing connections between power spectra of time series signals and clinical characteristics; these connections are key to understanding biological pathways through which sleep affects, and can be treated to improve, health. Such analyses are challenging as they must overcome the complicated structure of a power spectrum from multiple time series as a complex positive-definite matrix-valued function. This article proposes a new approach to such analyses based on a tensor-product spline model of Cholesky components of outcome-dependent power spectra. The approach flexibly models power spectra as nonparametric functions of frequency and outcome while preserving geometric constraints. Formulated in a fully Bayesian framework, a Whittle likelihood based Markov chain Monte Carlo (MCMC) algorithm is developed for automated model fitting and for conducting inference on associations between outcomes and spectral measures. The method is used to analyze data from a study of sleep in older adults and uncovers new insights into how stress and arousal are connected to the amount of time one spends in bed.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
