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A Depth for Censured Functional Data

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  • Paganoni, Anna M.
  • Sangalli, Laura M.

Abstract

Censured functional data are becoming more recurrent in applications. In those cases, the existing depth measure are useless. In this paper, an approach for measuring depths of censured functional data is presented. Its performance for finite samples is tested by simulation, showing that the new depth agrees with a integrated depth for uncensured functional data.

Suggested Citation

  • Paganoni, Anna M. & Sangalli, Laura M., 2019. "A Depth for Censured Functional Data," DES - Working Papers. Statistics and Econometrics. WS 28579, Universidad Carlos III de Madrid. Departamento de Estadística.
    • Handle: RePEc:cte:wsrepe:28579
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    References listed on IDEAS

    as
    1. Gerda Claeskens & Mia Hubert & Leen Slaets & Kaveh Vakili, 2014. "Multivariate Functional Halfspace Depth," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(505), pages 411-423, March.
    2. Aurore Delaigle & Peter Hall, 2013. "Classification Using Censored Functional Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(504), pages 1269-1283, December.
    3. A. Delaigle & P. Hall, 2016. "Approximating fragmented functional data by segments of Markov chains," Biometrika, Biometrika Trust, vol. 103(4), pages 779-799.
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    Keywords

    Functional Data;

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