对于时间序列模型的强大的两步波形基推理
Stéphane Guerrier1, Roberto Molinari2, Maria-Pia Victoria-Feser1
1University of Geneva, Geneva, Switzerland.
Journal of the American Statistical Association
|January 23, 2025
概括
本研究引入了潜在时间序列模型的强大的两步估计框架,解决了异常值和计算复杂性等挑战. 这种新方法增强了各种科学和经济领域的数据分析.
科学领域:
- 统计 统计 统计 统计
- 时间序列分析时间序列分析
- 信号处理 信号处理
背景情况:
- 隐性时间序列模型,包括自回归移动平均 (ARMA) 模型,在生物学,生态学,工程学和经济学中至关重要.
- 分析这些模型的挑战包括数据异常值,大型数据集的高计算成本和复杂的模型选择.
- 现有的方法往往无法同时解决这些问题.
研究的目的:
- 提出一个总体框架,用于对潜伏时间序列模型进行可靠的两步估计.
- 共同应对异常值,计算复杂性和模型选择等挑战.
- 为分析复杂时间序列数据提供实用和高效的方法.
主要方法:
- 开发一个有边界的影响M估计器波波幅变异处理异常值.
- 确定用于推断的波形变量估计器的联合异面正常性的条件.
- 应用波束时刻 (GMWM) 的通用方法,以进行可靠的两步估计.
主要成果:
- 拟议的强大的两步估计框架有效地处理异常值,并减少计算复杂性.
- 强大的估计器的异面性质是使用GMWM框架来得出的.
- 模拟研究表明,强大的GMWM估计器具有良好的有限样本性能.
结论:
- 开发的框架为潜在时间序列分析中常见的挑战提供了同时解决方案.
- 强大的GMWM估计器实际上是相关的,并在模拟中表现良好.
- 这种方法提高了时间序列分析在各种科学领域的可靠性和效率.
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