ArticleMultivariate behavioral research
Subgrouping with Chain Graphical VAR Models.
Article in Multivariate behavioral research. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
6 citing papers in PubMed.
- Robustness, Generalizability, and Heterogeneity of Dynamic Networks of Psychopathology.Cognitive therapy and research · 2026Article
- Transdiagnostic Network Mapping of Psychopathology in Daily Life: Rationale and Research Protocol.Clinical psychology in Europe · 2025Article
- Idiographic Lapse Prediction With State Space Modeling: Algorithm Development and Validation Study.JMIR formative research · 2025Observational
- Unsupervised Model Construction in Continuous-Time.Structural equation modeling : a multidisciplinary journal · 2025Article
- On Subgrouping Continuous Processes in Discrete Time.Multivariate behavioral researchArticle
- Evaluating Discrete Time Methods for Subgrouping Continuous Processes.Multivariate behavioral researchArticle
Corrections and comments
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Authors and funding
4 authors.
Funding
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.