在实验中复制非静止时间序列的期待
Zeda Li1, Yu Ryan Yue1, Scott A Bruce2
1Baruch College, The City University of New York.
The annals of applied statistics
|March 4, 2024
概括
本研究引入了一种新的功率分析 (ANOPOW) 模型,用于非静止时间序列中的时间变化的频率模式. 该模型有效地比较了随时间和频率的群体效应,帮助实验数据分析.
科学领域:
- 统计 统计 统计 统计
- 时间序列分析时间序列分析
- 信号处理 信号处理
背景情况:
- 复制的非静止时间序列在实验研究中很常见.
- 在此类数据中分析时间变化的频率模式存在挑战.
- 现有的方法可能无法充分捕捉动态组效应.
研究的目的:
- 为复制的非静止时间序列提出一种新的功率分析 (ANOPOW) 模型.
- 为了能够在不同群体中比较时间变化的第二阶段频率模式.
- 以时间和频率的函数来估计群体效应.
主要方法:
- 开发一个局部静止的ANOPOW Cramér光谱表示.
- 贝叶斯框架利用独立的二维二次随机步行 (RW2D) 先验.
- 局部时间变化的光谱估计的零碎静止近似.
- 集成嵌套拉普拉斯近似 (INLA) 用于后部分布计算.
主要成果:
- 拟议的ANOPOW模型有效地分析复制的非静止时间序列.
- 它允许灵活和适应性地平滑时间变化的功能效果.
- 这样可以准确地估计跨时间和频率的群体效应.
- 该模型在地震信号和ADHD瞳孔直径数据分析中展示了实用性.
结论:
- 新的ANOPOW模型为分析复杂时间序列数据提供了一个强大的框架.
- 它使用INLA提供了一个计算效率高的贝叶斯方法.
- 该模型适用于需要分析动态群体效应的各种实验环境.
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