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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2505.21490 |
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| _version_ | 1866912398356512768 |
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| author | Dolkar, Tsering Ferreira, Marco A. R. Shin, Hwasoo Tegge, Allison N. |
| author_facet | Dolkar, Tsering Ferreira, Marco A. R. Shin, Hwasoo Tegge, Allison N. |
| contents | We propose novel Bayesian Dynamic Clustering Factor Models (BDCFM) for the analysis of multivariate longitudinal data. BDCFM combines factor models with hidden Markov models to concomitantly perform dimension reduction, clustering, and estimation of the dynamic transitions of subjects through clusters. We develop an efficient Gibbs sampler for exploration of the posterior distribution. An analysis of a simulated dataset shows that our inferential approach works well both at parameter estimation and clustering of subjects. Finally, we illustrate the utility of our BDCFM with an analysis of a dataset on opioid use disorder. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_21490 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Bayesian Dynamic Clustering Factor Models Dolkar, Tsering Ferreira, Marco A. R. Shin, Hwasoo Tegge, Allison N. Methodology We propose novel Bayesian Dynamic Clustering Factor Models (BDCFM) for the analysis of multivariate longitudinal data. BDCFM combines factor models with hidden Markov models to concomitantly perform dimension reduction, clustering, and estimation of the dynamic transitions of subjects through clusters. We develop an efficient Gibbs sampler for exploration of the posterior distribution. An analysis of a simulated dataset shows that our inferential approach works well both at parameter estimation and clustering of subjects. Finally, we illustrate the utility of our BDCFM with an analysis of a dataset on opioid use disorder. |
| title | Bayesian Dynamic Clustering Factor Models |
| topic | Methodology |
| url | https://arxiv.org/abs/2505.21490 |