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快速多级功能主成分分析

Fast Multilevel Functional Principal Component Analysis.

作者信息

Cui Erjia, Li Ruonan, Crainiceanu Ciprian M, Xiao Luo

机构信息

Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, 615 N. Wolfe Street, Baltimore, MD 21205.

Department of Statistics, North Carolina State University, 2311 Stinson Dr, Raleigh, NC 27607.

出版信息

J Comput Graph Stat. 2023;32(2):366-377. doi: 10.1080/10618600.2022.2115500. Epub 2022 Oct 7.

Abstract

We introduce fast multilevel functional principal component analysis (fast MFPCA), which scales up to high dimensional functional data measured at multiple visits. The new approach is orders of magnitude faster than and achieves comparable estimation accuracy with the original MFPCA (Di et al., 2009). Methods are motivated by the National Health and Nutritional Examination Survey (NHANES), which contains minute-level physical activity information of more than 10000 participants over multiple days and 1440 observations per day. While MFPCA takes more than five days to analyze these data, fast MFPCA takes less than five minutes. A theoretical study of the proposed method is also provided. The associated function mfpca.face() is available in the R package refund.

摘要

我们引入了快速多级函数主成分分析(fast MFPCA),它可以扩展到在多次访视中测量的高维函数数据。新方法比原始的MFPCA(Di等人,2009年)快几个数量级,并且在估计精度上相当。这些方法的灵感来自于国家健康与营养检查调查(NHANES),该调查包含了10000多名参与者多天的分钟级身体活动信息,每天有1440次观测。虽然MFPCA分析这些数据需要五天多的时间,但fast MFPCA不到五分钟就能完成。我们还提供了对所提出方法的理论研究。相关的函数mfpca.face()可在R包refund中获取。

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