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
Wearable accelerometers for measuring and monitoring the motor behaviour of infants with brain damage during CareToy-Revised training
Silvia Filogna,Giada Martini,Elena Beani,Martina Maselli,Matteo Cianchetti,Nevio Dubbini,Giovanni Cioni,Giuseppina Sgandurra,Claudia Artese,Veronica Barzacchi,Alessandra Cecchi,Marta Cervo,Maria Luce Cioni,P. Dario,M. Di Galante,Ugo Faraguna,Patrizio Fiorini,Viola Fortini,Matteo Giampietri,Simona Giustini,Clara Lunardi,Irene Mannari,Valentina Menici,Letizia Padrini,Filomena Paternoster,Riccardo Rizzi +25 more
TL;DR: In this paper , the authors proposed an approach to predict clinical assessment scores of infants' motor activity using accelerometers placed on infants' wrists and trunk during playtime, based on the method of functional data analysis.
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Functional sufficient dimension reduction based on weighted method
TL;DR: Wang et al. as mentioned in this paper proposed a robust version of FSIR called the soft weighted FSIR (SFSIR), which used a trimmed and spatial median estimate to replace the classic moment estimation.
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P-spline smoothed functional ICA of EEG data
TL;DR: In this article, a functional independent component analysis (FICA) based on the use of fourth moments is proposed to estimate brain electrical activity sources from EEG signals, which is motivated by mapping adverse artifactual events caused by body movements and physiological activity originated outside the brain.
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Dynamic prediction with time‐dependent marker in survival analysis using supervised functional principal component analysis
Haolun Shi,Shu Jiang,Jiguo Cao +2 more
TL;DR: In this article , a novel supervised functional principal component analysis (FPCA) is proposed, where the functional principal components are determined to optimize the association between the time-varying biomarker and time-to-event outcome.
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Testing Stability in Functional Event Observations with an Application to IPO Performance
TL;DR: In this paper , the authors propose a change point analysis that has two steps, in which the first step segments the series into segments in which frequency of events is approximately homogeneous using a new binary segmentation procedure for event frequencies.
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