Subgrouping with Chain Graphical VAR Models
Subgrouping with Chain Graphical VAR Models
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DOI：
10.1080/00273171.2023.2289058
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发表时间：
2023-11-28
影响因子：
3.8
通讯作者：
Molenaar，Peter C. M.
中科院分区：
文献类型：
--
作者：
Park，Jonathan J.;Chow，Sy-Miin;Molenaar，Peter C. M.
近年来，出现了一种“idio-thetic”类方法，以弥合本体推理和具体推理之间的差距。这些方法通过汇集个体之间的个体内部信息来告知群体水平的推断，从而描述具体过程中的本体趋势，反之亦然。目前的工作介绍了一种新的“idio-thetic”模型：子分组链图形向量自回归（scGVAR）。scGVAR的独特之处在于它能够识别在滞后效应(1)和同步效应中共享共同动态网络结构的个体亚群。蒙特卡罗模拟的结果表明，当个体集群的同期动态不同时，scGVAR比类似的方法更有希望，并且在检测细微群体差异时显示出更高的灵敏度，同时保持较低的i型错误率。相比之下，另一种竞争方法——交替最小二乘VAR （ALS VAR）在分组距离较大时表现良好。进一步考虑ALS VAR和scGVAR在实际数据上的应用，以及两种方法的优势和局限性。
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.