A Bayesian multiple imputation approach to bivariate functional data with missing components

• Published in: Statistics in medicine Vol. 40; no. 22; pp. 4772 - 4793
• Main Authors: Jang, Jeong Hoon, Manatunga, Amita K., Chang, Changgee, Long, Qi
• Format: Journal Article
• Language: English
• Published: England Wiley Subscription Services, Inc 30.09.2021
• Subjects: Algorithms; Bayes Theorem; Bayesian latent factor model; bivariate functional data; Computer Simulation; curves; Data Interpretation, Statistical; Humans; Missing data; multiple imputation; Bayesian latent factor model; multiple imputation; bivariate functional data; curves; missing data
• ISSN: 0277-6715; 1097-0258; 1097-0258
• DOI: 10.1002/sim.9093

Existing missing data methods for functional data mainly focus on reconstructing missing measurements along a single function—a univariate functional data setting. Motivated by a renal study, we focus on a bivariate functional data setting, where each sampling unit is a collection of two distinct co...