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Classification rules based on distribution functions of functional depth

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  • Olusola Samuel Makinde

    (Federal University of Technology)

Abstract

In ordering multivariate objects, the use of data depth provides a centre-outward ranking. The notion of data depth has been extended to functional data setting and applied in classifying functional data, for example maximal depth classification rules. In this paper, we explore notions of functional depth and propose a classification method based on distribution functions of data depth for functional data. The performance of this method is examined by using simulations and real data sets and the results are compared with the results from existing methods.

Suggested Citation

  • Olusola Samuel Makinde, 2019. "Classification rules based on distribution functions of functional depth," Statistical Papers, Springer, vol. 60(3), pages 629-640, June.
    • Handle: RePEc:spr:stpapr:v:60:y:2019:i:3:d:10.1007_s00362-016-0841-0
    DOI: 10.1007/s00362-016-0841-0
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    References listed on IDEAS

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    Cited by:

    1. Zhou, Xinyu & Ma, Yijia & Wu, Wei, 2023. "Statistical depth for point process via the isometric log-ratio transformation," Computational Statistics & Data Analysis, Elsevier, vol. 187(C).

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