Unsupervised Model Construction in Continuous-Time
Bibliographic Data
| ID | 12424908 |
|---|---|
| Authors | Jonathan Park (0000-0002-6636-3504, University of California, Davis, corresponding author), Zachary F Fisher (0000-0003-2744-5141, Pennsylvania State University), Michael D Hunter (0000-0002-3651-6709, Pennsylvania State University), Chad E Shenk (0000-0001-6700-5656, University of Rochester), Michael A Russell (0000-0002-3956-604X, Pennsylvania State University), Peter C M Molenaar (0000-0002-0026-0756, Pennsylvania State University), Sy‐miin Chow (0000-0003-1938-027X, Pennsylvania State University) |
| Year | 2024 |
| Volume | 32 |
| Issue | 3 |
| Pages | 377-399 |
| Publication date | 2024-12-16 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Structural Equation Modeling: A Multidisciplinary Journal (JOURNAL) |
| Journal identifiers | ISSN: 1070-5511 • E-ISSN: 1532-8007 |
| Publisher | Taylor & Francis (PUBLISHER • GB) |
| DOI | 10.1080/10705511.2024.2429544 |
| PMID | 40386487 |
| OpenAlex | W4405428923 |
| Language | EN |
| Citations received | 1 |
| References cited | 65 |
Many of the advancements reconciling individual- and group-level results have occurred in the context of a discrete-time modeling framework. Discrete-time models are intuitive and offer relatively simple interpretations for the resulting dynamic structures; however, they do not possess the flexibility of models fitted in the continuous-time framework. We introduce ct-gimme, a continuous-time extension of the group iterative multiple model estimation (GIMME; Gates & Molenaar, 2012) procedure which enables researchers to fit complex, high dimensional dynamic networks in continuous-time. Our results indicate that ct-gimme outperforms N = 1 model fitting in continuous-time by pooling information across multiple subjects. Likewise, ct-gimme outperforms group-level model fitting in the presence of within-sample heterogeneity. We conclude with an empirical illustration and highlight limitations of the approach relating to identification of meaningful starting values
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| Unique citing works | 1 |
|---|---|
| Citations per year | 1 |
| Citation span | 2025 - 2025 (1) |
| Citation velocity | recent |
| Highly cited | No |