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使用交叉验证方法来选择时间序列模型:承诺和陷
1Human Development and Family Studies, Department of Human Ecology, University of California at Davis, Davis, California, USA.
The British journal of mathematical and statistical psychology
|December 7, 2023
概括
交叉验证 (CV) 方法对于评估心理学中的时间序列模型至关重要. 封闭的CV通常优于传统的信息标准,如AIC和BIC,用于评估预测错误,特别是在有限的数据的情况下.
科学领域:
- 心理学 心理学 心理学
- 统计 统计 统计 统计
- 时间序列分析时间序列分析
背景情况:
- 矢量自回归 (VAR) 建模在心理学中用于时间序列分析是常见的.
- 心理学研究中的短时间序列往往导致VAR模型过拟合和预测差.
- 建议进行交叉验证 (CV) 来评估模型的预测能力,但其与心理时间序列数据的性能尚不清楚.
研究的目的:
- 检查10倍CV和阻塞CV如何估计人均,AR和VAR模型的预测错误.
- 评估数据特征对CV方法性能的影响.
- 为了比较CV方法与Akaike (AIC) 和贝叶斯 (BIC) 模型选择信息标准.
主要方法:
- 模拟研究分析了三个时间序列模型 (人-平均,AR,VAR) 的预测错误.
- 评估两种交叉验证技术:十倍CV和封闭CV.
- 将CV方法与传统模型选择标准 (AIC,BIC) 的比较.
主要成果:
- CV方法显示,对于更简单的模型 (人-平均,AR) 预测错误的低估趋势.
- CV方法倾向于高估VAR模型的预测错误,特别是在小样本大小的情况下.
- 封闭的CV在选择最具预测性的模型方面普遍优于AIC和BIC.
结论:
- 交叉验证,特别是阻塞的简历,是评估时间序列模型在心理学中的预测准确性的宝贵工具.
- 在模型选择方面,CV方法比AIC和BIC具有优势,尽管样本规模小,可能存在偏差.
- 为心理时间序列分析中CV的实际应用提供了指导方针.
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