Functional Data Analysis
TLDR
In this article, the authors provide an overview of FDA, starting with simple statistical notions such as mean and covariance functions, then covering some core techniques, the most popular of which is functional principal component analysis (FPCA).Abstract:
With the advance of modern technology, more and more data are being recorded continuously during a time interval or intermittently at several discrete time points. These are both examples of functional data, which has become a commonly encountered type of data. Functional data analysis (FDA) encompasses the statistical methodology for such data. Broadly interpreted, FDA deals with the analysis and theory of data that are in the form of functions. This paper provides an overview of FDA, starting with simple statistical notions such as mean and covariance functions, then covering some core techniques, the most popular of which is functional principal component analysis (FPCA). FPCA is an important dimension reduction tool, and in sparse data situations it can be used to impute functional data that are sparsely observed. Other dimension reduction approaches are also discussed. In addition, we review another core technique, functional linear regression, as well as clustering and classification of functional d...read more
Citations
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Journal ArticleDOI
A functional data analysis approach for continuous 2-D emotion annotations
Karan Sharma,Marius Wagner,Claudio Castellini,Egon L. van den Broek,Freek Stulp,Friedhelm Schwenker +5 more
TL;DR: This article builds on the previous work by presenting a novel Functional Data Analysis based approach to assess the quality of annotations, where the bivariate annotation time-series are transformed into functions, such that each resulting functional annotation then becomes a sample element for analysis like MFPCA that evaluate variation across all annotations.
Journal ArticleDOI
Functional ensemble survival tree: Dynamic prediction of Alzheimer’s disease progression accommodating multiple time-varying covariates
TL;DR: A functional ensemble survival tree framework to characterize the joint effects of both functional and baseline covariates in predicting disease progression and a fast implementation of the algorithm that accommodates personalized dynamic prediction that can be updated as new observations are gathered to reflect the patient’s latest prognosis.
Book ChapterDOI
Introduction: Tracing the History of a Discipline Through Quantitative and Qualitative Analyses of Scientific Literature
TL;DR: In this article, the authors studied the temporal evolution of word occurrences in papers published by scientific journals and identified the main subject matters that were considered relevant by mainstream journals and offered new viewpoints to read and understand the evolution of a discipline.
Journal ArticleDOI
Functional regression on remote sensing data in oceanography
TL;DR: In this article, the authors proposed the use of a functional data analysis approach as an alternative to the classical statistical methods most commonly used in oceanography and water quality management, in particular the prediction of total suspended solids (TSS) based on remote sensing (RS) data.
Journal ArticleDOI
Network Functional Varying Coefficient Model
TL;DR: In this article, the authors consider functional responses with network dependence observed for each individual at irregular time points and model both the interindividual dependence and within-individual dynamic correlatio-...
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