Functional Principal Component Analysis for Extrapolating Multistream Longitudinal Data
Functional Principal Component Analysis for Extrapolating Multistream Longitudinal Data
复制标题
DOI：
10.1109/tr.2020.3035084
复制
发表时间：
2019-03
影响因子：
5.9
通讯作者：
Seokhyun Chung;R. Kontar
中科院分区：
文献类型：
--
作者：
Seokhyun Chung;R. Kontar
在这篇文章中，我们提出了一个非参数的方法来预测多流纵向数据的演变。我们的方法首先将每个流分解为特征函数及其相应的功能主成分（FPC）分数的线性组合。一个高斯过程之前的FPC分数，然后诱导基于一个功能的半度量，介绍了跨流的相似性度量。最后，一个经验贝叶斯更新策略推导出更新建立的先验使用实时流数据。经验证据表明，该框架优于国家的最先进的方法，可以有效地考虑异质性，以及实现高的预测精度。
In this article, we present a nonparametric approach to predict the evolution of multistream longitudinal data. Our approach first decomposes each stream into a linear combination of eigenfunctions and their corresponding functional principal component (FPC) scores. A Gaussian process prior for the FPC scores is then induced based on a functional semimetric that introduces a similarity measure across streams. Finally, an empirical Bayesian updating strategy is derived to update the established prior using real-time stream data. Empirical evidence shows that the proposed framework outperforms state-of-the-art approaches and can effectively account for heterogeneity as well as achieve high predictive accuracy.