ArticlePsychometrika2022
Penalized Estimation and Forecasting of Multiple Subject Intensive Longitudinal Data.
Article in Psychometrika, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed.
- An investigation into in-sample and out-of-sample model selection for nonstationary autoregressive models.The British journal of mathematical and statistical psychology · 2026Article
- Beyond the cross-section: Rethinking the intention-behaviour gap through a conceptual and methodological lens.British journal of health psychology · 2026Review
- Group-to-individual generalizability and individual-level inferences in cognitive neuroscience.Neuroscience and biobehavioral reviews · 2025Review
- What ergodicity means for you.Developmental cognitive neuroscience · 2024Review
- Guest Editors' Introduction to the Special Issue on Forecasting with Intensive Longitudinal Data.Psychometrika · 2022Article
- Evaluating Discrete Time Methods for Subgrouping Continuous Processes.Multivariate behavioral researchArticle
- Subgrouping with Chain Graphical VAR Models.Multivariate behavioral researchArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Intensive longitudinal data (ILD) is an increasingly common data type in the social and behavioral sciences. Despite the many benefits these data provide, little work has been dedicated to realize the potential such data hold for forecasting dynamic processes at the individual level. To address this gap in the literature, we present the multi-VAR framework, a novel methodological approach allowing for penalized estimation of ILD collected from multiple individuals. Importantly, our approach estimates models for all individuals simultaneously and is capable of adaptively adjusting to the amount of heterogeneity present across individual dynamic processes. To accomplish this, we propose a novel proximal gradient descent algorithm for solving the multi-VAR problem and prove the consistency of the recovered transition matrices. We evaluate the forecasting performance of our method in comparison with a number of benchmark methods and provide an illustrative example involving the day-to-day emotional experiences of 16 individuals over an 11-week period.
Indexed as
Identifiers
35060013What OpenQuestion holds
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.