单变量和多变量相干函数:效用和误解

R. Panerai, E. Katsogridakis, D. Simpson
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引用次数: 0

摘要

相干函数(CF)常用于评估心血管变量之间线性关系的显著性,但将其应用于压力感受器敏感性和动态大脑自调节研究的文献显示其有效性的统计局限性。因此,由于拒绝有用的数据和/或研究参与者,可能会引入偏差。多变量分析可以扩展CF的效用,允许识别对复杂调节控制机制有贡献的主要变量,也可以作为非线性系统分析的必要步骤。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Uni- and multi-variate coherence function: Utility and misconceptions
The coherence function (CF) is often used to assess the significance of linear relationships between cardiovascular variables, but the literature on applications to baroreceptor sensitivity and dynamic cerebral autoregulation studies shows statistical limitations in its validation. As a consequence, biases can be introduced due to rejection of useful data and/or study participants. Multivariate analysis can extend the utility of CF by allowing the identification of the main variables contributing to complex regulatory control mechanisms and also as a necessary step in the analysis of nonlinear systems.
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