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Comments on: Shape-based functional data analysis

Author

Listed:
  • J. E. Borgert

    (University of North Carolina at Chapel Hill)

  • J. S. Marron

    (University of North Carolina at Chapel Hill)

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.

Suggested Citation

  • J. E. Borgert & J. S. Marron, 2024. "Comments on: Shape-based functional data analysis," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 33(1), pages 66-70, March.
    • Handle: RePEc:spr:testjl:v:33:y:2024:i:1:d:10.1007_s11749-023-00914-6
    DOI: 10.1007/s11749-023-00914-6
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    References listed on IDEAS

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    1. Sungkyu Jung & Ian L. Dryden & J. S. Marron, 2012. "Analysis of principal nested spheres," Biometrika, Biometrika Trust, vol. 99(3), pages 551-568.
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