MULTILEVEL FUNCTIONAL PRINCIPAL COMPONENT ANALYSIS
MULTILEVEL FUNCTIONAL PRINCIPAL COMPONENT ANALYSIS
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DOI：
10.1214/08-aoas206
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发表时间：
2009-03-01
影响因子：
1.8
通讯作者：
Punjabi, Naresh M.
中科院分区：
文献类型：
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作者：
Di, Chong-Zhi;Crainiceanu, Ciprian M.;Punjabi, Naresh M.
睡眠心脏健康研究（SHHS）是一项关于睡眠及其对健康结果影响的综合性里程碑式研究。SHHS的主要指标是家庭多导睡眠图，包括两次访视时每个受试者的两个脑电图（EEG）通道。这些数据的数量和重要性给分析带来了巨大的挑战。为了解决这些挑战，我们引入了多级功能主成分分析（MFPCA），一种新的统计方法，旨在提取核心的内部和跨学科的几何成分的多级功能数据。虽然由SHHS的动机，提出的方法是普遍适用的，与许多现代科学研究的层次或纵向功能的结果具有潜在的相关性。值得注意的是，使用MFPCA，我们识别和量化睡眠期间EEG活动与不良心血管结局之间的关联。
The Sleep Heart Health Study (SHHS) is a comprehensive landmark study of sleep and its impacts on health outcomes. A primary metric of the SHHS is the in-home polysomnogram, which includes two electroencephalographic (EEG) channels for each subject, at two visits. The Volume and importance of this data presents enormous challenges for analysis. To address these challenges, we introduce multilevel functional principal component analysis (MFPCA), a novel statistical methodology designed to extract core intra- and inter-subject geometric components of multilevel functional data. Though motivated by the SHHS, the proposed methodology is generally applicable, with potential relevance to many modern scientific studies of hierarchical or longitudinal functional outcomes. Notably, using MFPCA, we identify and quantify associations between EEG activity during sleep and adverse cardiovascular outcomes.