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Activity Number: 111 - Application and Development of Statistical Methods for Spatio-Temporal Data
Type: Contributed
Date/Time: Monday, August 8, 2022 : 8:30 AM to 10:20 AM
Sponsor: Section on Bayesian Statistical Science
Abstract #322085
Title: Bayesian Functional Principal Components Analysis Using Relaxed Mutually Orthogonal Processes
Author(s): James Matuk* and Amy Herring and David Dunson
Companies: Duke University and Duke University and Duke University
Keywords: Functional Principal Components Analysis; Bayesian Analysis

Functional Principal Component Analysis (FPCA) is a prominent tool to characterize variability and reduce dimension of longitudinal and functional datasets. Bayesian implementations of FPCA are advantageous because of their ability to propagate uncertainty in subsequent modeling. To ease computation, many modeling approaches rely on the restrictive assumption that functional principal components can be represented through a pre-fixed basis. Alternatively, we propose a flexible Bayesian FPCA model using Relaxed Mutually Orthogonal (ReMO) processes. We define ReMO processes to enforce mutual orthogonality between principal components to ensure identifiability of model parameters. The joint distribution of ReMO processes is governed by a penalty parameter that determines the degree to which the processes are mutually orthogonal and is related to ease of posterior computation. We demonstrate our proposed model using extensive simulation experiments and in an application to study the effects of breastfeeding status, illness, and demographic factors on weight dynamics in early childhood.

Authors who are presenting talks have a * after their name.

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