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

ABSTRACT: 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 component functions, one of which may be missing. Specifically, we propose a Bayesian multiple imputation approach based on a bivariate functional latent factor model that exploits the joint changing patterns of the component functions to allow accurate and stable imputation of one component given the other. We further extend the framework to address multilevel bivariate functional data with missing components by modeling and exploiting inter-component and intra-subject correlations. We develop a

SUBMITTER: Jang JH 

PROVIDER: S-EPMC9125166 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature