对非静止自动回归模型的样本内和样本外模型选择的调查
Yong Zhang1, Anja F Ernst1, Ginette Lafit2
1Department of Psychometrics and Statistics, University of Groningen, Groningen, the Netherlands.
The British journal of mathematical and statistical psychology
|October 29, 2025
概括
选择最好的时间序列模型在心理学研究中至关重要. 贝叶斯信息标准 (BIC) 通常在非静止过程中表现最好,但理论驱动的方法应该补充数据驱动的方法.
科学领域:
- 心理学 心理学 心理学
- 时间序列分析 时间序列分析
- 统计建模 统计建模
背景情况:
- 静态自回归模型是心理学时间序列分析的基础.
- 非静止模型捕捉了不断变化的时间动态,但缺乏明确的选择指导.
- 准确的模型选择对于理解心理时间序列数据至关重要.
研究的目的:
- 评估非静止时间序列的样本内和样本外模型选择技术.
- 在模拟的非静止过程中比较不同选择方法的性能.
- 为心理研究中选择合适的非静止时间序列模型提供指导.
主要方法:
- 一项模拟研究评估了六个无变异的非静止过程的模型选择性能.
- 评估了样本内 (信息标准) 和样本外 (交叉验证,预测) 方法.
- 重新分析了影响数据的真实世界时间序列,以说明模型选择.
主要成果:
- 贝叶斯信息标准 (BIC) 在模型选择中显示出最佳的整体性能.
- 其他选择技术的有效性取决于时间序列的长度.
- 在不同的非静止工艺类型中,模型选择性能差异很大.
结论:
- 仅仅基于数据的模型选择对非静止时间序列来说是不够的.
- 将理论驱动的见解与数据驱动的方法相结合,可以提高模型选择的准确性.
- 对于非静止时间序列分析,建议采用混合方法,将定性理解和定量方法结合起来.
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