A Geometric Approach to Pairwise Bayesian Alignment of Functional Data Using Importance Sampling

• Activity Number: 90 - Invited EPoster Session
• Type: Invited
• Date/Time: Sunday, July 28, 2019 : 8:30 PM to 10:30 PM
• Sponsor: Section on Statistics in Imaging
• Author(s): Sebastian Kurtek*
• Companies: Ohio State University
• Keywords: functional data; Bayesian registration model; amplitude and phase; square-root velocity function

Abstract

We present a Bayesian model for pairwise nonlinear registration of functional data. We use the Riemannian geometry of the space of warping functions to define appropriate prior distributions and sample from the posterior using importance sampling. A simple square-root transformation is used to simplify the geometry of the space of warping functions, which allows for computation of sample statistics, such as the mean and median, and a fast implementation of a k-means clustering algorithm. These tools allow for efficient posterior inference, where multiple modes of the posterior distribution corresponding to multiple plausible alignments of the given functions are found. We also show pointwise 95% credible intervals to assess the uncertainty of the alignment in different clusters. We validate this model using simulations and present multiple examples on real data from different application domains including biometrics and medicine.