使用链式图形 VAR 模型进行分组。

IF 5.3 3区 心理学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
Multivariate Behavioral Research Pub Date : 2024-05-01 Epub Date: 2024-02-13 DOI:10.1080/00273171.2023.2289058
Jonathan J Park, Sy-Miin Chow, Sacha Epskamp, Peter C M Molenaar
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引用次数: 0

摘要

近年来,出现了一类 "特异推理 "方法,以弥补提名推理和特异推理之间的差距。这些方法通过汇集跨个体的个体内信息来为群体层面的推断提供信息,反之亦然,从而描述特异过程中的提名趋势。目前的工作引入了一种新颖的 "特异性 "模型:分组链图向量自回归(scGVAR)。scGVAR 的独特之处在于它能够识别在滞后效应(1)和同期效应中具有共同动态网络结构的个体子群。蒙特卡洛模拟结果表明,当个体集群的同期动态存在差异时,scGVAR 有望超越类似方法,并在检测细微群体差异方面显示出更高的灵敏度,同时保持较低的类型一误差率。相比之下,一种与之竞争的方法--交替最小二乘法 VAR(ALS VAR)--在组间距离较大的情况下表现良好。本文还就 ALS VAR 和 scGVAR 在实际数据中的应用以及这两种方法的优势和局限性做了进一步的探讨。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Subgrouping with Chain Graphical VAR Models.

Recent years have seen the emergence of an "idio-thetic" class of methods to bridge the gap between nomothetic and idiographic inference. These methods describe nomothetic trends in idiographic processes by pooling intraindividual information across individuals to inform group-level inference or vice versa. The current work introduces a novel "idio-thetic" model: the subgrouped chain graphical vector autoregression (scGVAR). The scGVAR is unique in its ability to identify subgroups of individuals who share common dynamic network structures in both lag(1) and contemporaneous effects. Results from Monte Carlo simulations indicate that the scGVAR shows promise over similar approaches when clusters of individuals differ in their contemporaneous dynamics and in showing increased sensitivity in detecting nuanced group differences while keeping Type-I error rates low. In contrast, a competing approach-the Alternating Least Squares VAR (ALS VAR) performs well when groups were separated by larger distances. Further considerations are provided regarding applications of the ALS VAR and scGVAR on real data and the strengths and limitations of both methods.

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来源期刊
Multivariate Behavioral Research
Multivariate Behavioral Research 数学-数学跨学科应用
CiteScore
7.60
自引率
2.60%
发文量
49
审稿时长
>12 weeks
期刊介绍: Multivariate Behavioral Research (MBR) publishes a variety of substantive, methodological, and theoretical articles in all areas of the social and behavioral sciences. Most MBR articles fall into one of two categories. Substantive articles report on applications of sophisticated multivariate research methods to study topics of substantive interest in personality, health, intelligence, industrial/organizational, and other behavioral science areas. Methodological articles present and/or evaluate new developments in multivariate methods, or address methodological issues in current research. We also encourage submission of integrative articles related to pedagogy involving multivariate research methods, and to historical treatments of interest and relevance to multivariate research methods.
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