时间顺序选择对基于矢量自回归模型集群密集的纵向数据的影响
Yaqi Li1, Hairong Song2, Bertus Jeronimus3
1Department of Pediatrics, Health Sciences Center, University of Oklahoma.
Psychological methods
|March 3, 2025
概括
最低顺序 (LO) 方法在识别纵向数据中的集群方面通常优于最高顺序 (HO). 将LO方法与高斯混合模型 (GMM) 结合起来,为集群识别带来了最好的结果.
科学领域:
- 纵向数据分析 纵向数据分析
- 统计建模 统计建模
- 心理测量 心理测量 心理测量
背景情况:
- 多变量强度纵向数据的基于模型的聚类依赖于跨个体一致的时间顺序.
- 心理和行为过程往往在时间顺序上表现出个体之间的差异.
- 设定时间顺序的现有方法包括对所有过程使用最高顺序 (HO) 或最低顺序 (LO),对其影响的研究有限.
研究的目的:
- 检查HO和LO方法在基于矢量自回归 (VAR) 集群中的性能.
- 在基于VAR的两步集群程序中比较高斯混合模型 (GMM) 和k-means算法.
- 根据其纵向数据动态,确定对个人集群的最佳方法.
主要方法:
- 进行了一项模拟研究,以评估HO和LO方法的性能.
- 这项研究使用了高斯混合模型 (GMM) 和k-means集群算法.
- 在各种数据条件中实施了基于向量自回归 (VAR) 的两步集群程序.
主要成果:
- 与最高顺序 (HO) 方法相比,最低顺序 (LO) 方法在集群识别中表现优越.
- 最高顺序 (HO) 方法对估计集群特定动态更为有利.
- 高斯混合模型 (GMM) 通常表现优于k-平均集群.
- 结合LO方法和GMM方法的结合为集群识别带来了最好的结果.
结论:
- 时间顺序的选择 (LO与HO) 显著影响基于VAR的聚类结果.
- 建议将高斯混合模型 (GMM) 与LO方法相结合,以在纵向数据中进行可靠的集群识别.
- 结果为基于模型的聚类技术的实证应用提供了实际建议.
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