• Corpus ID: 249062677

Bayesian Functional Principal Component Analysis using Relaxed Mutually Orthogonal Processes

  title={Bayesian Functional Principal Component Analysis using Relaxed Mutually Orthogonal Processes},
  author={James Matuk and Amy H. Herring and David B. Dunson},
Functional Principal Component Analysis (FPCA) is a prominent tool to characterize variability and reduce dimension of longitudinal and functional datasets. Bayesian implementations of FPCA are advantageous because of their ability to propagate uncertainty in subsequent modeling. To ease computation, many modeling approaches rely on the restrictive assumption that functional principal components can be represented through a pre-specified basis. Under this assumption, inference is sensitive to… 


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Functional Data Analysis
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  • Computer Science
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  • 2011
An overview of functional data analysis is provided, 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).
Functional Data Analysis, Springer Series in Statistics, Springer
  • 2005
A probability path, Springer
  • 2019