自相关时间序列与短距离依赖之间的等级交叉相关性的显著性测试
David Lun1, Svenja Fischer2, Alberto Viglione3
1Institute of Hydraulic Engineering and Water Resources Management, Vienna University of Technology, Vienna, Austria.
Journal of applied statistics
|October 9, 2023
概括
这项研究引入了一种新的统计测试,以准确分析时间序列数据,考虑自相关性. 改进的方法避免了虚假的相关性,为洪水和温度模式等环境数据提供了可靠的见解.
科学领域:
- 环境科学 环境科学
- 统计 统计 统计 统计
- 时间序列分析时间序列分析
背景情况:
- 像肯德尔的Tau和斯皮尔曼的Rho这样的统计依赖度在环境数据分析中很常见.
- 数据的自相关性可能导致误导性的交叉相关性,使准确的分析变得复杂.
- 现有的方法可能无法充分解决自身相关性,可能产生虚假的结果.
研究的目的:
- 呈现Spearman的Rho和Kendall的Tau估计器对自身相关时间序列的非对称分布.
- 为了使在存在自相关性时,能够对交叉相关性进行可靠的统计假设测试.
- 为分析环境时间序列数据提供更可靠的方法.
主要方法:
- 使用U统计学推导估计者的非对称分布.
- 假设绝对规律的 (β混合) 过程,包括各种短程依赖模型.
- 开发适用于不指定确切的随机模型的假设测试.
主要成果:
- 拟议的测试有效地考虑了自身相关性,减轻了虚假的交叉相关性.
- 模拟表明,使用常见的随机模型和适度的样本大小,修改后的测试具有更高的性能.
- 对欧洲洪水和温度数据的应用揭示了标准测试的虚假发现,新测试纠正了这些错误.
结论:
- 开发的统计测试为分析自身相关的环境时间序列提供了更准确的方法.
- 这种方法提高了检测诸如洪水和温度等变量之间的真实关系的可靠性.
- 这些发现与欧洲洪水管理制度变化的现有文献一致,验证了测试的有效性.
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