ArticleBiometrika2024
Individualized dynamic latent factor model for multi-resolutional data with application to mobile health.
Article in Biometrika, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Representation Retrieval Learning for Heterogeneous Data Integration.Journal of the American Statistical Association · 2026Article
- A causal discovery framework for digital phenotyping.Scientific reports · 2026Article
- Physical activity buffers physiological stress during high emotional distress: a wearable-derived prospective cohort study.medRxiv : the preprint server for health sciences · 2026Article
- Individualized Time-Varying Nonparametric Model With an Application in Mobile Health.Statistics in medicine · 2025Article
- Effectiveness of PEER Intervention on Older Adults' Physical Activity Time Series Using Smoothing Spline ANOVA.Mathematics (Basel, Switzerland) · 2025Article
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Authors and funding
5 authors.
Funding
Abstract
Mobile health has emerged as a major success for tracking individual health status, due to the popularity and power of smartphones and wearable devices. This has also brought great challenges in handling heterogeneous, multi-resolution data that arise ubiquitously in mobile health due to irregular multivariate measurements collected from individuals. In this paper, we propose an individualized dynamic latent factor model for irregular multi-resolution time series data to interpolate unsampled measurements of time series with low resolution. One major advantage of the proposed method is the capability to integrate multiple irregular time series and multiple subjects by mapping the multi-resolution data to the latent space. In addition, the proposed individualized dynamic latent factor model is applicable to capturing heterogeneous longitudinal information through individualized dynamic latent factors. Our theory provides a bound on the integrated interpolation error and the convergence rate for
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.