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Comments on: Exploratory functional data analysis

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  • Rob J. Hyndman

    (Monash University)

Abstract

A useful approach to exploratory functional data analysis is to work in the lower-dimensional principal component space rather than in the original functional data space. I demonstrate this approach by finding anomalies in age-specific US mortality rates between 1933 and 2022. The same approach can be employed for many other standard data analysis tasks and has the advantage that it allows immediate use of the vast array of multivariate data analysis tools that already exist, rather than having to develop new tools for functional data.

Suggested Citation

  • Rob J. Hyndman, 2025. "Comments on: Exploratory functional data analysis," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 34(2), pages 483-487, June.
    • Handle: RePEc:spr:testjl:v:34:y:2025:i:2:d:10.1007_s11749-025-00963-z
    DOI: 10.1007/s11749-025-00963-z
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    1. Zhuo Qu & Wenlin Dai & Carolina Euan & Ying Sun & Marc G. Genton, 2025. "Exploratory functional data analysis," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 34(2), pages 459-482, June.
    2. Zhuo Qu & Wenlin Dai & Carolina Euan & Ying Sun & Marc G. Genton, 2025. "Rejoinder to the discussion on “Exploratory Functional Data Analysis”," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 34(2), pages 502-507, June.
    3. Dai, Wenlin & Genton, Marc G., 2019. "Directional outlyingness for multivariate functional data," Computational Statistics & Data Analysis, Elsevier, vol. 131(C), pages 50-65.
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