Longitudinal Functional Principal Component Analysis

被引：0
|
作者：
Greven, Sonja [1 ]
Crainiceanu, Ciprian [2 ]
Caffo, Brian [2 ]
Reich, Daniel [3 ]
机构：
[1] Ludwig Maximilians Univ Munchen, Munich, Germany
[2] Johns Hopkins Univ, Baltimore, MD USA
[3] NIH, Bethesda, MD 20892 USA
来源：
RECENT ADVANCES IN FUNCTIONAL DATA ANALYSIS AND RELATED TOPICS | 2011年
关键词：
EFFECTS MODELS;
D O I：
暂无
中图分类号：
O1 [数学];
学科分类号：
0701 ; 070101 ;
摘要：
We introduce models for the analysis of functional data observed at multiple time points. The model can be viewed as the functional analog of the classical mixed effects model where random effects are replaced by random processes. Computational feasibility is assured by using principal component bases. The methodology is motivated by and applied to a diffusion tensor imaging (DTI) study on multiple sclerosis.
引用
收藏
页码：149 / 154
页数：6
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