Subgrouping with Chain Graphical VAR Models.

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  • Additional Information
    • Source:
      Publisher: Taylor & Francis Group Country of Publication: United States NLM ID: 0046052 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1532-7906 (Electronic) Linking ISSN: 00273171 NLM ISO Abbreviation: Multivariate Behav Res Subsets: MEDLINE
    • Publication Information:
      Publication: <2009- > : Philadelphia, PA : Taylor & Francis Group
      Original Publication: Fort Worth, Tex. : Society of Multivariate Experimental Psychology
    • Subject Terms:
      Monte Carlo Method* ; Models, Statistical* ; Computer Simulation*/statistics & numerical data; Humans ; Data Interpretation, Statistical ; Least-Squares Analysis
    • Abstract:
      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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    • Grant Information:
      U24 AA027684 United States AA NIAAA NIH HHS; UL1 TR002014 United States TR NCATS NIH HHS
    • Contributed Indexing:
      Keywords: Community detection; dynamic network modeling; psychopathology; vector autoregression
    • Publication Date:
      Date Created: 20240214 Date Completed: 20240618 Latest Revision: 20240622
    • Publication Date:
      20240622
    • Accession Number:
      PMC11187704
    • Accession Number:
      10.1080/00273171.2023.2289058
    • Accession Number:
      38351547