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Refining Relational Event Models: Bayesian Penalization and Variable Selection in REMs — arXiv:2608.28419

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.28419
License (as archived): CC0-1.0
Archived: 2026-10-17T11:15:07.091Z
SHA-256 of the archived text: 62e625e7b0a34c5673210ec12f55c5f90eb5470683707773b4bddbe113ff3c6d
Stable URL of this page: https://metraipolis.com/documento/dc_7051761804b6

How to cite

Refining Relational Event Models: Bayesian Penalization and Variable Selection in REMs — arXiv:2608.28419 — arXiv (en), https://arxiv.org/abs/2608.28419 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_7051761804b6 (Metraipolis archive dc_7051761804b6, sha256 62e625e7b0a34c5673210ec12f55c5f90eb5470683707773b4bddbe113ff3c6d)

BibTeX

@misc{dc7051761804b6,
  title = {Refining Relational Event Models: Bayesian Penalization and Variable Selection in REMs — arXiv:2608.28419},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.28419},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_7051761804b6; sha256 62e625e7b0a34c5673210ec12f55c5f90eb5470683707773b4bddbe113ff3c6d},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.28419. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Relational Event Models (REMs) provide valuable insights into the dynamics of longitudinal social networks. Yet, the vast availability of potential predictors for a dyad's event rate poses the risk of selecting irrelevant variables and specifying an overfitted model that does not generalize to new data. Despite the recent popularity of Bayesian regularization methods to address this, there has not been a systematic evaluation of the performance of Exact Bayesian Regularization (EBR) and Approximate Bayesian Regularization (ABR) against standard Maximum Likelihood (ML) procedures commonly used for REMs. To address this, we conduct a simulation study in which directed Relational Event History (REH) data is generated with endogenous and exogenous effects of varying strength and compare the performance of (a) unregularized ML estimation, (b) ABR through normal approximations of the likelihood with Ridge and Horseshoe priors, and (c) EBR with Horseshoe priors. We find that threshold-based criteria applied to ABR and EBR with Horseshoe priors outperform univariate selection using p-values from ML estimation in variable selection accuracy. Regarding predictive performance, ABR with Ridge priors and EBR with Horseshoe priors outperform ML REMs, particularly for small sample sizes. Given only small gains with large computational burdens when using EBR, we therefore advise researchers to use ABR with a suitable prior.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-08-31 in stat.ME).
Categorie arXiv: Methodology (stat.ME) ; Computation (stat.CO).
Pagina del lavoro: https://arxiv.org/abs/2608.28419 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-17T10:57:31.076Z dalla pagina https://arxiv.org/catchup/stat.ME/2026-08-31?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).