Longitudinal Functional Data Analysis.
Longitudinal Functional Data Analysis.
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DOI：
10.1002/sta4.89
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发表时间：
2015
期刊：
影响因子：
--
通讯作者：
Staicu AM
中科院分区：
文献类型：
--
作者：
Park SY;Staicu AM
由于基于纵向的设计，我们考虑了相关的依赖功能数据：在重复的时间观察每个受试者，每次记录功能观察（曲线）。我们提出了一种新的简化建模框架，用于重复观测的功能观测，允许提取低维特征。所提出的方法考虑了纵向设计，旨在研究底层过程的动态行为，允许预测完整的未来轨迹，并且计算速度快。研究了该框架的理论性质，数值研究证实了该框架在有限样本中的良好性能。提出的方法是由一个扩散张量成像研究多发性硬化症的动机和应用。
We consider dependent functional data that are correlated because of a longitudinal-based design: each subject is observed at repeated times and at each time a functional observation (curve) is recorded. We propose a novel parsimonious modeling framework for repeatedly observed functional observations that allows to extract low dimensional features. The proposed methodology accounts for the longitudinal design, is designed to study the dynamic behavior of the underlying process, allows prediction of full future trajectory, and is computationally fast. Theoretical properties of this framework are studied and numerical investigations confirm excellent behavior in finite samples. The proposed method is motivated by and applied to a diffusion tensor imaging study of multiple sclerosis.