Low-Rank Covariance Function Estimation for Multidimensional Functional Data

• Activity Number: 443 - Student Paper Competition Presentations
• Type: Topic Contributed
• Date/Time: Thursday, August 6, 2020 : 10:00 AM to 11:50 AM
• Sponsor: Section on Nonparametric Statistics
• Author(s): Jiayi Wang* and Raymond K. W. Wong and Xiaoke Zhang
• Companies: Texas A&M University and Texas A&M University and George Washington University
• Keywords: multidimensional functional data; low-rank estimation; tensor product space; multi-linear rank; phase transition

Abstract

Multidimensional functional data are becoming more common in various domains such as climate studies, neuroimaging, and chemometrics. In this talk, I will present a nonparametric covariance function estimation approach under the framework of reproducing kernel Hilbert spaces (RKHS) that can handle both sparse and dense functional data. It has low-rank structures in both eigen-components of covariance function and marginal structures. Also, I will discuss the corresponding numerical results and the unified convergence theory.