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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A general framework for functional regression modelling
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TL;DR: A comprehensive framework for additive (mixed) models for functional responses and/or functional covariates based on the guiding principle of reframing functional regression in terms of corresponding models for scalar data is discussed, allowing the adaptation of a large body of existing methods for these novel tasks.
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Dimensionality reduction of diffusion MRI measures for improved tractometry of the human brain
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TL;DR: It is demonstrated that dMRI analyses can benefit from dimensionality reduction techniques, to help disentangling the neurobiological underpinnings of white matter organisation.
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TL;DR: Higher SBPV in the first 24 hours of admission was associated with unfavorable in-hospital outcome among ICH patients, and further prospective studies are warranted to understand any cause-effect relationship and whether controlling forSBPV may improve the ICH outcome.
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Grouped functional time series forecasting: An application to age-specific mortality rates
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Nonparametric Analysis of Thermal Proteome Profiles Reveals Novel Drug-binding Proteins.
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