INTRINSIC RIEMANNIAN FUNCTIONAL DATA ANALYSIS FOR SPARSE LONGITUDINAL OBSERVATIONS

• 成果类型: Article
• 署名作者: Shao, Lingxuan; Lin, Zhenhua; Yao, Fang
• 署名单位: Peking University; National University of Singapore
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/22-AOS2172
• 发表日期: 2022
• 页码: 1696-1721
• 关键词: Principal component analysis; extrinsic sample means; Nonparametric Regression; statistical-analysis; Fréchet Regression; convergence-rates; MANIFOLDS; covariance; FRAMEWORK; matrices

摘要

A new framework is developed to intrinsically analyze sparsely observed Riemannian functional data. It features four innovative components: a frame-independent covariance function, a smooth vector bundle termed covariance vector bundle, a parallel transport and a smooth bundle metric on the covariance vector bundle. The introduced intrinsic covariance function links estimation of covariance structure to smoothing problems that involve raw covariance observations derived from sparsely observed Riemannian functional data, while the covariance vector bundle provides a rigorous mathematical foundation for formulating such smoothing problems. The parallel transport and the bundle metric together make it possible to measure fidelity of fit to the covariance function. They also play a critical role in quantifying the quality of estimators for the covariance function. As an illustration, based on the proposed framework, we develop a local linear smoothing estimator for the covariance function, analyze its theoretical properties and provide numerical demonstration via simulated and real data sets. The intrinsic feature of the framework makes it applicable to not only Euclidean submanifolds but also manifolds without a canonical ambient space.