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在离散时间中缺失数据生态瞬间评估数据的状态空间建模:蒙特卡洛对计算方法的研究

L R Slipetz¹, A Falk¹, T R Henry¹

¹Department of Psychology, University of Virginia, Charlottesville, USA.

Multivariate behavioral research

|March 17, 2025

概括

生态瞬间评估 (EMA) 研究经常面临缺失的数据. 卡尔曼波器有效地处理状态空间模型中缺失的数据,优于时间序列分析的多重归算方法.

科学领域:

心理学科学心理学科学
统计建模统计建模
数据科学数据科学数据科学

背景情况:

缺少数据是生态瞬间评估 (EMA) 中的一个重大挑战,原因是参与者 attrition.
强大的缺失数据处理对于EMA研究中可靠的时间序列分析至关重要.

研究的目的:

调查用于处理缺失数据的方法,在意义上的离散时间连续测量状态空间模型在EMA使用.
为了比较不同缺失数据机制和归算技术的性能.

主要方法:

用状态空间模型建模的EMA数据的时间序列分析.
缺失数据机制的评估:完全随机缺失 (MCAR),随机缺失 (MAR),时间依赖的随机缺失 (T-MAR),自行回归的时间依赖的随机缺失 (AR-T-MAR) 和不随机缺失 (MNAR).
卡尔曼波器和多重归算技术的应用和比较.

主要成果:

与AR-T-MAR和MNAR相比,MCAR,MAR和T-MAR数据的偏差和变异性较小.
卡尔曼波器在各种条件下处理缺失数据方面表现出卓越的性能.
多种归算方法难以准确地恢复模型参数,与现有文献相反.

结论:

卡尔曼波器是管理EMA状态空间模型中缺失数据的高效工具.
研究人员应该仔细考虑缺失的数据机制和归算策略,因为多重归算可能对这些模型来说不是最佳的.

关键词:

缺少的数据数据.生态瞬间评估环境瞬间评估国家空间模型.时间序列时间序列

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