Bayesian Framework for Simultaneous Registration and Estimation of Noisy, Sparse and Fragmented Functional Data.

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 c

SUBMITTER: Matuk J 

PROVIDER: S-EPMC10027387 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature