用于各种应用的时间尺度调查的校准EWMA估计器
Abdullah Mohammed Alomair1, Soofia Iftikhar2
1Department of Quantitative Methods, School of Business, King Faisal University, Al-Ahsa 31982, Saudi Arabia.
Heliyon
|May 28, 2024
概括
使用指数加权移动平均线 (EWMA) 的新校准估计器提高了分层随机采样的准确性. 这些EWMA估计器的性能优于股票市场和天气数据分析的现有方法.
科学领域:
- 统计 统计 统计 统计
- 数据分析 数据分析
- 时间序列分析时间序列分析
背景情况:
- 记忆类型的平均值估计器使用指数加权移动平均值 (EWMA) 来整合历史和当前数据.
- 现有的EWMA估计器包括比率,乘积和对数类型.
研究的目的:
- 为单层和双层随机抽样提出新的EWMA类型校准估计器.
- 通过校准方法利用补充信息来提高估计准确度.
主要方法:
- 开发EWMA类型的校准估计器,用于分层随机抽样.
- 使用现实世界时间尺度的股票市场和天气数据集进行评估.
- 模拟研究使用双变的对称数据集.
主要成果:
- 拟议的EWMA校准估计器显示出卓越的性能.
- 数字结果证实了新估计器的有效性,而不是适应的估计器.
- 校准方法通过调整分层重量来提高估计值.
结论:
- 提出的EWMA校准估计器在分层随机抽样中提供了更高的准确性.
- 这些估计器对于分析真实世界的时间尺度数据是有效的.
- 校准技术为基于EWMA的估计提供了宝贵的增强.
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