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Expectile regression for spatial functional data analysis (sFDA)

Author

Listed:
  • Mustapha Rachdi

    (Univ. Grenoble Alpes)

  • Ali Laksaci

    (King Khalid University)

  • Noriah M. Al-Kandari

    (Kuwait University)

Abstract

This paper deals with the nonparametric estimation of the expectile regression when the observations are spatially correlated and are of a functional nature. The main findings of this work is the establishment of the almost complete convergence for the proposed estimator under some general mixing conditions. The performance of the proposed estimator is examined by using simulated data. Finally, the studied model is used to evaluate the air quality indicators in northeast China.

Suggested Citation

  • Mustapha Rachdi & Ali Laksaci & Noriah M. Al-Kandari, 2022. "Expectile regression for spatial functional data analysis (sFDA)," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 85(5), pages 627-655, July.
    • Handle: RePEc:spr:metrik:v:85:y:2022:i:5:d:10.1007_s00184-021-00846-x
    DOI: 10.1007/s00184-021-00846-x
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    References listed on IDEAS

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    Cited by:

    1. Larbi Ait-Hennani & Zoulikha Kaid & Ali Laksaci & Mustapha Rachdi, 2022. "Nonparametric Estimation of the Expected Shortfall Regression for Quasi-Associated Functional Data," Mathematics, MDPI, vol. 10(23), pages 1-23, November.

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