对于有限数据的转移
1University of Hong Kong, University of Hong Kong, University of Hong Kong, Institute of Data Science, Hong Kong SAR, China; Department of Urban Planning and Design, Hong Kong SAR, China; and Urban Systems Institute, Hong Kong SAR, China.
本研究引入了对离散数据的新转移度,克服了小或高卡丁度时间序列分析中的偏差和显著性问题. 它可以在没有模拟的情况下进行可靠的信息流评估.
科学领域:
- 复杂系统分析 复杂系统分析
- 信息理论是信息理论.
- 时间序列分析时间序列分析.
背景情况:
- 转移量化了定向的信息流,但面临着连续数据的挑战.
- 对于离散数据,它遭受了稀疏计数的积极偏差,缺乏统计学意义评估.
- 现有的方法在有限的数据流中扎,这些数据流的大小小小或核心值很高.
研究的目的:
- 为有限的离散数据流开发一种新的传输测量方法.
- 解决现有估计器的局限性,特别是偏差和缺乏意义测试.
- 为了使非参数统计学显著性评估,而不依赖于模拟.
主要方法:
- 通过计算有限数据流中的信息内容,推导出一种新的传输度.
- 避免明确考虑符号作为随机变量.
- 确保了对标准插件估计器的非对称性.
主要成果:
- 新的测量方法在异面上相当于标准的插件估计器.
- 它有效地纠正了与稀疏垃圾箱计数相关的积极偏见问题.
- 它允许对有限时间序列的统计显著性的完全非参数评估.
- 该方法适用于小尺寸和/或高枢纽度的时间序列.
结论:
- 拟议的转移量为分析离散的有限数据中的信息流提供了可靠的解决方案.
- 它克服了传统方法的关键局限性,提高了可靠性和可解释性.
- 允许在复杂系统中对指导信息传输进行严格的统计验证.
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