和复杂性的联合非对称分布
Angelika Silbernagel1, Christian H Weiß1
1Department of Mathematics and Statistics, Helmut Schmidt University, 22043 Hamburg, Germany.
Chaos (Woodbury, N.Y.)
|February 23, 2026
概括
这项研究分析了弱依赖下的时间序列中的顺序模式频率和复杂性. 它提供了测试序列依赖和估计复杂数据分析不确定性的新方法.
科学领域:
- 时间序列分析时间序列分析
- 统计推理 统计推理
- 信息理论 信息理论
背景情况:
- 顺序模式对于时间序列分析至关重要.
- 复杂度指标总结了时间序列动态.
- 了解非对称分布是统计推理的关键.
研究的目的:
- 导出顺序模式频率和复杂性的非对称分布.
- 调查各种过程的长期共变矩阵.
- 开发和评估测试串行依赖的测试.
主要方法:
- 非对称的分布理论.
- 移动平均线,高斯式和通用硬币投过程的分析导数.
- 基于模拟的近似和有限样本的性能检查.
主要成果:
- 建立了顺序模式频率和复杂度对的非对称分布.
- 导出分析和近似的长期协差矩阵.
- 开发了一种串行依赖测试,并对有限样本的性能进行评估.
结论:
- 由此得出的非对称结果有助于理解时间序列属性.
- 该研究为串行依赖测试和不确定性估计提供了工具.
- 这项工作推进了分析复杂时间序列数据的统计方法.
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