在具有随机系数的线性过程中估计变化点的平均值权力的加权和
Yi Wu1, Wei Wang1, Shipeng Wu2
1School of Big Data and Artificial Intelligence, Chizhou University, Chizhou, People's Republic of China.
Journal of applied statistics
|December 4, 2024
概括
本研究引入了一种新方法,即平均值权力加权总和 (WSPM) 估计器,用于检测线性过程中的平均值变化. WSPM估计器在复杂数据序列中识别变化点方面表现出可靠的性能.
科学领域:
- 统计 统计 统计 统计
- 时间序列分析时间序列分析
- 计量经济学 计量经济学
背景情况:
- 检测时间序列数据中的平均变化对于理解动态系统至关重要.
- 现有的方法可能面临着依赖随机变量和随机系数的局限性.
研究的目的:
- 为具有随机系数和未知的平均位移的线性过程提出一种新的变化点估计器.
- 严格分析拟议估计器的统计属性.
主要方法:
- 平均权力的加权总和 (WSPM) 估计器的发展.
- 理论分析确定弱和强的一致性属性.
- 通过模拟研究和真实世界数据分析进行实证验证.
主要成果:
- 在温和条件下,WSPM估计器被证明是弱和强的一致性.
- 对于WSPM估计器的弱一致率是理论上确定的.
- 模拟结果和真实数据应用证实了WSPM方法的优势.
结论:
- 拟议的WSPM估计器为线性过程中变化点检测提供了强大的和有效的方法.
- 理论保证和经验证据支持WSPM估计器在各种应用中的实际实用性.
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