Electronic Journal of Statistics

A geometric approach to pairwise Bayesian alignment of functional data using importance sampling

Sebastian Kurtek

Full-text: Open access

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.

Article information

Source
Electron. J. Statist. Volume 11, Number 1 (2017), 502-531.

Dates
Received: March 2016
First available in Project Euclid: 2 March 2017

Permanent link to this document
https://projecteuclid.org/euclid.ejs/1488423806

Digital Object Identifier
doi:10.1214/17-EJS1243

Subjects
Primary: 62F15: Bayesian inference

Keywords
Functional data warping function Bayesian registration model square-root slope function square-root density

Rights
Creative Commons Attribution 4.0 International License.

Citation

Kurtek, Sebastian. A geometric approach to pairwise Bayesian alignment of functional data using importance sampling. Electron. J. Statist. 11 (2017), no. 1, 502--531. doi:10.1214/17-EJS1243. https://projecteuclid.org/euclid.ejs/1488423806.


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