ArticleFrontiers in psychology2016
Clustering Vector Autoregressive Models: Capturing Qualitative Differences in Within-Person Dynamics.
Article in Frontiers in psychology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Modeling qualitative between-person heterogeneity in time series using latent class vector autoregressive models.Behavior research methods · 2025Article
- Continuous-Time Modeling of the Bidirectional Relationship Between Incidental Affect and Physical Activity.Annals of behavioral medicine : a publication of the Society of Behavioral Medicine · 2022Article
- Network structure of time-varying depressive symptoms through dynamic time warp analysis in late-life depression.International journal of geriatric psychiatry · 2022Article
- A Systematic Study into the Factors that Affect the Predictive Accuracy of Multilevel VAR(1) Models.Psychometrika · 2022Article
- Article
- The network approach to psychopathology: a review of the literature 2008-2018 and an agenda for future research.Psychological medicine · 2020Review
- Identifying Qualitative Between-Subject and Within-Subject Variability: A Method for Clustering Regime-Switching Dynamics.Frontiers in psychology · 2020Article
- Affective family interactions and their associations with adolescent depression: A dynamic network approach.Development and psychopathology · 2018Article
- Subgrouping with Chain Graphical VAR Models.Multivariate behavioral researchArticle
- Evaluating Discrete Time Methods for Subgrouping Continuous Processes.Multivariate behavioral researchArticle
- Network Mapping with GIMME.Multivariate behavioral researchArticle
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4 authors.
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Abstract
In psychology, studying multivariate dynamical processes within a person is gaining ground. An increasingly often used method is vector autoregressive (VAR) modeling, in which each variable is regressed on all variables (including itself) at the previous time points. This approach reveals the temporal dynamics of a system of related variables across time. A follow-up question is how to analyze data of multiple persons in order to grasp similarities and individual differences in within-person dynamics. We focus on the case where these differences are qualitative in nature, implying that subgroups of persons can be identified. We present a method that clusters persons according to their VAR regression weights, and simultaneously fits a shared VAR model to all persons within a cluster. The performance of the algorithm is evaluated in a simulation study. Moreover, the method is illustrated by applying it to multivariate time series data on depression-related symptoms of young women.
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