ArticleJournal of statistical theory and practice2026
A Repeated Block Perturbation Subsampling for Large-Scale Longitudinal Data.
Article in Journal of statistical theory and practice, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
- A Perturbation Subsampling Method for Massive Censored Data.Entropy (Basel, Switzerland) · 2026Article
- A Repeated Block Perturbation Subsampling for Large-Scale Longitudinal Data.Journal of statistical theory and practice · 2026Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Large-scale longitudinal data are now commonly available due to technological advances and collaborative research efforts. One example is the use of mobile data collection app in healthcare studies (mHealth), which generates a large-scale longitudinal mHealth data. In this paper, we propose a repeated block perturbation subsampling algorithm for the analysis of large-scale longitudinal data based on generalized estimating equation. The proposed method simultaneously provides consistent point and variance estimators. We establish the asymptotic properties of the resulting subsampling estimators. We also examine the finite-sample performance of the proposed method with simulation studies and demonstrate its application using real mHealth data sets.
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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.