Longitudinal canonical correlation analysis

被引：1

作者：

Lee, Seonjoo ^{[1
,2
,4
]}

Choi, Jongwoo ^{[1
]}

Fang, Zhiqian ^{[1
]}

Bowman, F. DuBois ^{[3
]}

机构：

[1] New York State Psychiat Inst & Hosp, Mental Hlth Data Sci, New York, NY USA

[2] Columbia Univ, Dept Biostat & Psychiat, New York, NY USA

[3] Univ Michigan, Dept Biostat, Ann Arbor, MI USA

[4] 1051 Riverside Dr,Unit 48, New York, NY 10032 USA

来源：

JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES C-APPLIED STATISTICS | 2023年 / 72卷 / 03期

基金：

美国国家卫生研究院; 加拿大健康研究院;

关键词：

Alzheimer's disease; canonical correlation analysis; longitudinal data analysis; PRINCIPAL-COMPONENTS; REGIONS;

D O I：

10.1093/jrsssc/qlad022

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

This paper considers canonical correlation analysis for two longitudinal variables that are possibly sampled at different time resolutions with irregular grids. We modelled trajectories of the multivariate variables using random effects and found the most correlated sets of linear combinations in the latent space. Our numerical simulations showed that the longitudinal canonical correlation analysis (LCCA) effectively recovers underlying correlation patterns between two high-dimensional longitudinal data sets. We applied the proposed LCCA to data from the Alzheimer's Disease Neuroimaging Initiative and identified the longitudinal profiles of morphological brain changes and amyloid cumulation.

引用

页码：587 / 607

页数：21

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