实时检测密集的纵向数据中的关联变化:一个指数加权移动平均数程序
Evelien Schat1, Sarah Schrevens1, Francis Tuerlinckx1
1Quantitative Psychology and Individual Differences, Faculty of Psychology and Educational Sciences, KU Leuven, Leuven, Belgium.
The British journal of mathematical and statistical psychology
|February 24, 2026
概括
使用指数加权移动平均 (EWMA) 程序,可以实时检测恶化的幸福感. 建议在预计只有关联变化时监测皮尔森或斯皮尔曼相关性.
科学领域:
- 心理评估 心理评估
- 数据分析数据分析
- 时间序列分析时间序列分析.
背景情况:
- 个人内在的关联变化可能表明幸福感和功能下降.
- 需要实时检测方法来及时识别这些负变化.
研究的目的:
- 评估指数加权移动平均线 (EWMA) 程序对线性关联变化的实时检测的有效性.
- 评估平均值和方程变化对EWMA检测性能的影响.
主要方法:
- 使用各种措施 (皮尔森相关性,斯皮尔曼相关性,共变量,距离) 在时间窗口内计算关联强度.
- 使用EWMA监测这些关联得分的平均水平变化.
- 模拟场景与平均值,方差和相关性变化,以测试检测性能.
主要成果:
- 建议在预计只有关联变化时监测Pearson和Spearman与EWMA的相关性得分.
- 对平均值和方差转移敏感的替代关联测量可以在特定的组合变化场景下提高检测.
- 检测性能受到平均值,方差和相关性变化的相互作用的影响.
结论:
- EWMA是一种可行的实时检测心理关联变化的方法.
- 关联度的选择会影响检测准确度,这取决于数据变化的性质.
- 预测潜在的数据变化可以指导选择最佳的关联措施,以改善监测.
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