Comments on: Shape-based functional data analysis

J. E. Borgert () and J. S. Marron ()
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J. E. Borgert: University of North Carolina at Chapel Hill
J. S. Marron: University of North Carolina at Chapel Hill

TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, 2024, vol. 33, issue 1, No 5, 66-70

Abstract: Abstract This discussion paper applauds the authors for their impactful contribution to functional data analysis (FDA). Their primary insight lies in a formal mathematical definition of the “shape” of a curve, which they connect to familiar intuitive notions through a number of examples. Notably, the paper highlights the pitfalls of less well-thought-out curve registration approaches. The authors’ application of COVID-19 data enriches the discussion, highlighting the work’s practical relevance. We discuss connections of this work with object-oriented data analysis and propose enhancements to the authors’ shape-based functional principal component analysis. Additionally, we illustrate the practical significance of adaptive alignment with an example from our own research.

Keywords: Functional data analysis; Functional PCA; Object oriented data analysis; Scale space (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s11749-023-00914-6

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