ArticleJournal of the American Statistical Association2022
Bayesian Framework for Simultaneous Registration and Estimation of Noisy, Sparse and Fragmented Functional Data.
Article in Journal of the American Statistical Association, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Joint Registration and Conformal Prediction for Partially Observed Functional Data.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2026Article
- Enhancing Empathic Accuracy: Penalized Functional Alignment Method to Correct Temporal Misalignment in Real-Time Emotional Perception.Psychometrika · 2025Article
- Sequential Bayesian Registration for Functional Data.Statistics and computing · 2025Article
- Topo-Geometric Analysis of Variability in Point Clouds Using Persistence Landscapes.IEEE transactions on pattern analysis and machine intelligence · 2024Article
- Bayesian function registration with random truncation.PloS one · 2023Article
- Timeline Registration for Electronic Health Records.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2023Article
- Variograms for kriging and clustering of spatial functional data with phase variation.Spatial statistics · 2022Article
- Shape-restricted estimation and spatial clustering of COVID-19 infection rate curves.Spatial statistics · 2022Article
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Authors and funding
4 authors.
Funding
Abstract
In many applications, smooth processes generate data that is recorded under a variety of observational regimes, including dense sampling and sparse or fragmented observations that are often contaminated with error. The statistical goal of registering and estimating the individual underlying functions from discrete observations has thus far been mainly approached sequentially without formal uncertainty propagation, or in an application-specific manner by pooling information across subjects. We propose a unified Bayesian framework for simultaneous registration and estimation, which is flexible enough to accommodate inference on individual functions under general observational regimes. Our ability to do this relies on the specification of strongly informative prior models over the amplitude component of function variability using two strategies: a data-driven approach that defines an empirical basis for the amplitude subspace based on training data, and a shape-restricted approach when the relative location and number of extrema is well-understood. The proposed methods build on the elastic functional data analysis framework to separately model amplitude and phase variability inherent in functional data. We emphasize the importance of uncertainty quantification and visualization of these two components as they provide complementary information about the estimated functions. We validate the proposed framework using multiple simulation studies and real applications.
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