ArticleBiometrics2019
Registration for exponential family functional data.
Article in Biometrics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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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
16 citing papers in PubMed.
- Functional quantile principal component analysis.Biostatistics (Oxford, England) · 2025Article
- Regression and alignment for functional data and network topology.Biostatistics (Oxford, England) · 2025Article
- Functional Principal Component Analysis for Continuous Non-Gaussian, Truncated, and Discrete Functional Data.Statistics in medicine · 2024Article
- A RIEMANN MANIFOLD MODEL FRAMEWORK FOR LONGITUDINAL CHANGES IN PHYSICAL ACTIVITY PATTERNS.The annals of applied statistics · 2023Article
- Distributional data analysis via quantile functions and its application to modeling digital biomarkers of gait in Alzheimer's Disease.Biostatistics (Oxford, England) · 2023Article
- Timeline Registration for Electronic Health Records.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2023Article
- Constructing a polygenic risk score for childhood obesity using functional data analysis.Econometrics and statistics · 2023Article
- TWO-SAMPLE TESTS FOR MULTIVARIATE REPEATED MEASUREMENTS OF HISTOGRAM OBJECTS WITH APPLICATIONS TO WEARABLE DEVICE DATA.The annals of applied statistics · 2022Article
- Scalar on time-by-distribution regression and its application for modelling associations between daily-living physical activity and cognitive functions in Alzheimer's Disease.Scientific reports · 2022Article
- Quantifying and correcting slide-to-slide variation in multiplexed immunofluorescence images.Bioinformatics (Oxford, England) · 2022Article
- Registration of 24-hour accelerometric rest-activity profiles and its application to human chronotypes.Biological rhythm research · 2022Article
- Diurnal Physical Activity Patterns across Ages in a Large UK Based Cohort: The UK Biobank Study.Sensors (Basel, Switzerland) · 2021Article
- Intensity warping for multisite MRI harmonization.NeuroImage · 2020Article
- Nonnegative decomposition of functional count data.Biometrics · 2020Article
- Accelerometry data in health research: challenges and opportunities.Statistics in biosciences · 2019Article
- Scalar-on-function regression for predicting distal outcomes from intensively gathered longitudinal data: Interpretability for applied scientists.Statistics surveys · 2019Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
We introduce a novel method for separating amplitude and phase variability in exponential family functional data. Our method alternates between two steps: the first uses generalized functional principal components analysis to calculate template functions, and the second estimates smooth warping functions that map observed curves to templates. Existing approaches to registration have primarily focused on continuous functional observations, and the few approaches for discrete functional data require a pre-smoothing step; these methods are frequently computationally intensive. In contrast, we focus on the likelihood of the observed data and avoid the need for preprocessing, and we implement both steps of our algorithm in a computationally efficient way. Our motivation comes from the Baltimore Longitudinal Study on Aging, in which accelerometer data provides valuable insights into the timing of sedentary behavior. We analyze binary functional data with observations each minute over 24 hours for 592 participants, where values represent activity and inactivity. Diurnal patterns of activity are obscured due to misalignment in the original data but are clear after curves are aligned. Simulations designed to mimic the application indicate that the proposed methods outperform competing approaches in terms of estimation accuracy and computational efficiency. Code for our method and simulations is publicly available.
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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.