Longitudinal Canonical Correlation Analysis.

IF 1 4区数学 Q3 STATISTICS & PROBABILITY

Journal of the Royal Statistical Society Series C-Applied Statistics Pub Date : 2023-06-01 DOI:10.1093/jrsssc/qlad022

Seonjoo Lee, Jongwoo Choi, Zhiqian Fang, F DuBois Bowman

引用次数: 0

Abstract

This paper considers canonical correlation analysis for two longitudinal variables that are possibly sampled at different time resolutions with irregular grids. We modeled 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.

查看原文本刊更多论文

纵向典型相关分析。

本文研究了可能在不同时间分辨率下以不规则网格采样的两个纵向变量的典型相关分析。我们使用随机效应对多元变量的轨迹进行建模，并在潜在空间中找到最相关的线性组合集。数值模拟结果表明，纵向典型相关分析(LCCA)可以有效地恢复两个高维纵向数据集之间的潜在关联模式。我们将提出的LCCA应用于阿尔茨海默病神经影像学倡议的数据，并确定了脑形态变化和淀粉样蛋白积累的纵向分布。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Journal of the Royal Statistical Society Series C-Applied Statistics 数学-统计学与概率论

CiteScore

2.50

自引率

0.00%

发文量

审稿时长

>12 weeks

期刊介绍： The Journal of the Royal Statistical Society, Series C (Applied Statistics) is a journal of international repute for statisticians both inside and outside the academic world. The journal is concerned with papers which deal with novel solutions to real life statistical problems by adapting or developing methodology, or by demonstrating the proper application of new or existing statistical methods to them. At their heart therefore the papers in the journal are motivated by examples and statistical data of all kinds. The subject-matter covers the whole range of inter-disciplinary fields, e.g. applications in agriculture, genetics, industry, medicine and the physical sciences, and papers on design issues (e.g. in relation to experiments, surveys or observational studies). A deep understanding of statistical methodology is not necessary to appreciate the content. Although papers describing developments in statistical computing driven by practical examples are within its scope, the journal is not concerned with simply numerical illustrations or simulation studies. The emphasis of Series C is on case-studies of statistical analyses in practice.