ArticleStatistical modelling2016
Longitudinal Functional Models with Structured Penalties.
Article in Statistical modelling, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed.
- Contrastive Latent Functional Model.Statistics in biosciences · 2026Article
- Multilevel Multivariate Functional Principal Component Analysis of Evoked and Induced Event-Related Spectral Perturbations.Statistics in biosciences · 2025Article
- Central Posterior Envelopes for Bayesian Functional Principal Component Analysis.Journal of data science : JDS · 2023Article
- A functional model for studying common trends across trial time in eye tracking experiments.Statistics in biosciences · 2023Article
- Article
- Covariate-adjusted hybrid principal components analysis for region-referenced functional EEG data.Statistics and its interface · 2022Article
- Multilevel modeling of spatially nested functional data: Spatiotemporal patterns of hospitalization rates in the US dialysis population.Statistics in medicine · 2021Article
- Use of Functional Linear Models to Detect Associations between Characteristics of Walking and Continuous Responses Using Accelerometry Data.Sensors (Basel, Switzerland) · 2020Article
- Longitudinal dynamic functional regression.Journal of the Royal Statistical Society. Series C, Applied statistics · 2020Article
- Bayesian Semiparametric Functional Mixed Models for Serially Correlated Functional Data, with Application to Glaucoma Data.Journal of the American Statistical Association · 2019Article
- Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
This article addresses estimation in regression models for longitudinally-collected functional covariates (time-varying predictor curves) with a longitudinal scaler outcome. The framework consists of estimating a time-varying coefficient function that is modeled as a linear combination of time-invariant functions with time-varying coefficients. The model uses extrinsic information to inform the structure of the penalty, while the estimation procedure exploits the equivalence between penalized least squares estimation and a linear mixed model representation. The process is empirically evaluated with several simulations and it is applied to analyze the neurocognitive impairment of HIV patients and its association with longitudinally-collected magnetic resonance spectroscopy (MRS) curves.
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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.