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Activity Number: 245 - SLDS CSpeed 4
Type: Contributed
Date/Time: Wednesday, August 11, 2021 : 10:00 AM to 11:50 AM
Sponsor: Section on Statistical Learning and Data Science
Abstract #319108
Title: Latent Factor Model for Multivariate Functional Data
Author(s): Ruonan Li* and Luo Xiao
Companies: North Carolina State University and Department of Statistics, North Carolina State University
Keywords: Multivariate functional data ; Decomposition model; Latent factor model

For multivariate functional data with plentiful functional components, an efficient dimension reduction approach to capture features is desired. We propose a functional latent factor model, where complex covariance is decomposed into a shared term and an outcome-specific term. This model uses low dimensional latent factors along with coefficient vectors to automatically induce dependency among observed functions. For data with higher dimensions, a sparse structure in coefficient vectors is further considered. We illustrate the performance of the proposed model through simulation studies and an application to electroencephalogram(EEG) data obtained by 64 electrodes.

Authors who are presenting talks have a * after their name.

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