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Forecasting with Functional and Twice Censored Data

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  • Leulmi Sarra

    (University brothers Mentouri, Constantine 1)

Abstract

In this study, we propose a new kernel functional regression estimator when the random response variable is subject to twice censoring. Censoring is employed to handle cases where complete response data is unavailable, allowing for more robust and reliable statistical analysis. Our proposed estimator is specifically designed to provide accurate forecasts even in the presence of such incomplete data. Then, we investigate its mean square convergence, with rate. To reinforce the obtained results, we conduct numerical results to highlight the performance and the accuracy of our proposed estimator.

Suggested Citation

  • Leulmi Sarra, 2024. "Forecasting with Functional and Twice Censored Data," SN Operations Research Forum, Springer, vol. 5(4), pages 1-19, December.
    • Handle: RePEc:spr:snopef:v:5:y:2024:i:4:d:10.1007_s43069-024-00390-0
    DOI: 10.1007/s43069-024-00390-0
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    References listed on IDEAS

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    1. Pengfei Liu & Mengchen Zhang & Ru Zhang & Qin Zhou, 2021. "Robust Estimation and Tests for Parameters of Some Nonlinear Regression Models," Mathematics, MDPI, vol. 9(6), pages 1-16, March.
    2. Kebabi, Khedidja & Messaci, Fatiha, 2012. "Rate of the almost complete convergence of a kernel regression estimate with twice censored data," Statistics & Probability Letters, Elsevier, vol. 82(11), pages 1908-1913.
    3. Kitouni, Abderrahim & Boukeloua, Mohamed & Messaci, Fatiha, 2015. "Rate of strong consistency for nonparametric estimators based on twice censored data," Statistics & Probability Letters, Elsevier, vol. 96(C), pages 255-261.
    4. Fama, Eugene F. & French, Kenneth R., 1993. "Common risk factors in the returns on stocks and bonds," Journal of Financial Economics, Elsevier, vol. 33(1), pages 3-56, February.
    5. Nengxiang Ling & Yang Liu, 2017. "The kernel regression estimation for randomly censored functional stationary ergodic data," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 46(17), pages 8557-8574, September.
    6. Zhiyong Zhou & Zhengyan Lin, 2016. "Asymptotic normality of locally modelled regression estimator for functional data," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 28(1), pages 116-131, March.
    7. Fatiha Messaci & Nahima Nemouchi & Idir Ouassou & Mustapha Rachdi, 2015. "Local polynomial modelling of the conditional quantile for functional data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 24(4), pages 597-622, November.
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