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New DTVEM-RE model enables person-specific lag estimation in longitudinal data

By PulseAugur Editorial · [2 sources] ·

Researchers have developed DTVEM-RE, a novel extension to the Differential Time-Varying Effect Model (DTVEM) that allows for person-specific lag coefficients in intensive longitudinal data. This new model addresses the limitation of the original DTVEM, which assumed a uniform lag structure across all individuals. DTVEM-RE offers two versions for analysis: a discrete-time hierarchical Bayesian VAR in Stan and a continuous-time per-person Ornstein-Uhlenbeck model in ctsem. Simulations and application to an EMA dataset demonstrate DTVEM-RE's ability to accurately capture individual differences in lag effects and improve predictive accuracy. AI

IMPACT Enables more personalized analysis of time-series data, potentially improving clinical research and behavioral science insights.

RANK_REASON The cluster contains a research paper detailing a new statistical model extension.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New DTVEM-RE model enables person-specific lag estimation in longitudinal data

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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Amartya Bhattacharya ·

    DTVEM-RE: A Hierarchical Random-Effects Extension of the Differential Time-Varying Effect Model for Person-Specific Multi-Lag Estimation in Intensive Longitudinal Data

    arXiv:2606.14116v1 Announce Type: new Abstract: The Differential Time-Varying Effect Model (DTVEM) of Jacobson et al. (2019) is a popular tool for finding the best time lag in intensive longitudinal data, but it assumes everyone shares the same lag structure. The original authors…

  2. arXiv cs.LG TIER_1 English(EN) · Amartya Bhattacharya ·

    DTVEM-RE: A Hierarchical Random-Effects Extension of the Differential Time-Varying Effect Model for Person-Specific Multi-Lag Estimation in Intensive Longitudinal Data

    The Differential Time-Varying Effect Model (DTVEM) of Jacobson et al. (2019) is a popular tool for finding the best time lag in intensive longitudinal data, but it assumes everyone shares the same lag structure. The original authors named fixing this as future work, and it clashe…

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