Modeling sparse longitudinal data on Riemannian manifolds

• Published in: Biometrics Vol. 77; no. 4; pp. 1328 - 1341
• Main Authors: Dai, Xiongtao, Lin, Zhenhua, Müller, Hans‐Georg
• Format: Journal Article
• Language: English
• Published: United States Blackwell Publishing Ltd 01.12.2021
• Subjects: Algorithms; Alzheimer disease; Alzheimer Disease - diagnostic imaging; Alzheimer's disease; brain; Covariance; Data collection; Databases, Factual; Eigenvectors; functional data analysis; Humans; longitudinal compositional data; Mathematical analysis; Matrices (mathematics); Medical imaging; Modelling; Neural networks; Neurodegenerative diseases; Neuroimaging; neuroimaging studies; prediction; principal component analysis; Principal components analysis; Riemann manifold; sampling schemes; symmetric positive‐definite matrices; symmetric positive-definite matrices; sampling schemes; functional data analysis; longitudinal compositional data; neuroimaging studies; Alzheimer's disease; principal component analysis
• ISSN: 0006-341X; 1541-0420; 1541-0420
• DOI: 10.1111/biom.13385

Modern data collection often entails longitudinal repeated measurements that assume values on a Riemannian manifold. Analyzing such longitudinal Riemannian data is challenging, because of both the sparsity of the observations and the nonlinear manifold constraint. Addressing this challenge, we propo...