模型周期,趋势和时间变化的效应在动态结构方程模型与回归的模型
1Department of Psychology, Center for Lifespan Changes in Brain and Cognition, University of Oslo, Oslo, Norway.
Multivariate behavioral research
|June 2, 2025
概括
本研究将回归线引入到动态结构方程模型 (DSEM) 中,用于分析密集的纵向数据. 这种灵活的方法准确地捕捉非线性趋势和周期,改善了复杂数据中的参数估计.
科学领域:
- 量化心理学 量化心理学
- 统计建模 统计建模
- 纵向数据分析 纵向数据分析
背景情况:
- 密集的纵向数据 (ILD) 越来越普遍,使得超越传统生长模型的先进分析成为可能.
- 动态结构方程模型 (DSEM) 在ILD中很受欢迎,但与未指定的非线性效应作斗争.
- 在DSEM中准确地建模趋势,周期和时间变化的预测因素仍然是一个实际的挑战.
研究的目的:
- 引入回归线作为DSEM在ILD中模拟非线性效应的灵活工具.
- 为DSEM建模器提供一个构建块,以处理复杂的功能形状.
- 在DSEM中展示平滑先验和层次平滑术语的应用.
主要方法:
- 将回归线整合到DSEM框架中,以灵活地学习底层函数形状.
- 应用平滑先验和层次平滑术语来建模复杂的时间依赖.
- 模拟研究评估忽视非线性趋势对参数估计的影响.
主要成果:
- 忽视DSEM中的非线性趋势可能会导致偏差的参数估计.
- 回归线有效地捕捉非线性模式,例如每周周期和长期趋势.
- 拟议的框架成功地模拟了日记数据中的复杂时间动态.
结论:
- 回归线为DSEM提供了一个强大而灵活的扩展,用于分析具有非线性效应的ILD.
- 这种方法增强了DSEM工具包,允许更准确地建模趋势和周期.
- 该方法适用于现实世界日记数据,改善对酒精消费和压力动态的理解.
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