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On the local linear estimate for functional regression: Uniform in bandwidth consistency

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  • Mohammed Attouch
  • Ali Laksaci
  • Fatima Rafaa

Abstract

We consider the problem of local linear estimation of the regression function when the regressor is functional. The main result of this paper is to prove the strong convergence (with rates), uniformly in bandwidth parameters (UIB), of the considered estimator. The main interest of this result is the possibility to derive the asymptotic properties of our estimate even if the bandwidth parameter is a random variable.

Suggested Citation

  • Mohammed Attouch & Ali Laksaci & Fatima Rafaa, 2019. "On the local linear estimate for functional regression: Uniform in bandwidth consistency," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 48(8), pages 1836-1853, April.
    • Handle: RePEc:taf:lstaxx:v:48:y:2019:i:8:p:1836-1853
    DOI: 10.1080/03610926.2018.1440308
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    Cited by:

    1. Salim Bouzebda & Amel Nezzal & Tarek Zari, 2022. "Uniform Consistency for Functional Conditional U -Statistics Using Delta-Sequences," Mathematics, MDPI, vol. 11(1), pages 1-39, December.
    2. Salim Bouzebda & Inass Soukarieh, 2022. "Non-Parametric Conditional U -Processes for Locally Stationary Functional Random Fields under Stochastic Sampling Design," Mathematics, MDPI, vol. 11(1), pages 1-69, December.
    3. Mustapha Rachdi & Ali Laksaci & Zoulikha Kaid & Abbassia Benchiha & Fahimah A. Al‐Awadhi, 2021. "k‐Nearest neighbors local linear regression for functional and missing data at random," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 75(1), pages 42-65, February.
    4. Salim Bouzebda & Boutheina Nemouchi, 2023. "Weak-convergence of empirical conditional processes and conditional U-processes involving functional mixing data," Statistical Inference for Stochastic Processes, Springer, vol. 26(1), pages 33-88, April.

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