Longitudinal functional principal component analysis.
Longitudinal functional principal component analysis.
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DOI：
10.1214/10-ejs575
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发表时间：
2010
影响因子：
1.1
通讯作者：
Reich D
中科院分区：
文献类型：
--
作者：
Greven S;Crainiceanu C;Caffo B;Reich D
我们引入模型来分析在多个时间点观测到的功能数据。功能数据的动态行为被分解为随时间变化的总体平均值、基线（或静态）特定于受试者的可变性、纵向（或动态）特定于受试者的可变性、特定于受试者访问的可变性和测量误差。该模型可以看作是经典纵向混合效应模型的功能模拟，其中随机效应被随机过程所取代。该方法具有广泛的适用性，并且在计算上对中等和大型数据集是可行的。通过对函数过程采用主成分基，保证了计算的可行性。该方法源于一项弥散张量成像（DTI）研究，该研究旨在分析健康志愿者和多发性硬化症（MS）患者大脑连通性的差异和变化。提供了一个R实现。87
We introduce models for the analysis of functional data observed at multiple time points. The dynamic behavior of functional data is decomposed into a time-dependent population average, baseline (or static) subject-specific variability, longitudinal (or dynamic) subject-specific variability, subject-visit-specific variability and measurement error. The model can be viewed as the functional analog of the classical longitudinal mixed effects model where random effects are replaced by random processes. Methods have wide applicability and are computationally feasible for moderate and large data sets. Computational feasibility is assured by using principal component bases for the functional processes. The methodology is motivated by and applied to a diffusion tensor imaging (DTI) study designed to analyze differences and changes in brain connectivity in healthy volunteers and multiple sclerosis (MS) patients. An R implementation is provided. 87