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Least squares estimators of the regression function with twice censored data

Author

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  • Kebabi, K.
  • Laroussi, I.
  • Messaci, F.

Abstract

We propose least squares estimators of E(Y/X=x) for Y censored on the right by R and min(Y,R) left censored. We establish their convergence in the L2-norm. This work extends a known result in the context of right censoring.

Suggested Citation

  • Kebabi, K. & Laroussi, I. & Messaci, F., 2011. "Least squares estimators of the regression function with twice censored data," Statistics & Probability Letters, Elsevier, vol. 81(11), pages 1588-1593, November.
  • Handle: RePEc:eee:stapro:v:81:y:2011:i:11:p:1588-1593
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    References listed on IDEAS

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    1. Kohler, Michael & Máthé, Kinga & Pintér, Márta, 2002. "Prediction from Randomly Right Censored Data," Journal of Multivariate Analysis, Elsevier, vol. 80(1), pages 73-100, January.
    2. Messaci, Fatiha, 2010. "Local averaging estimates of the regression function with twice censored data," Statistics & Probability Letters, Elsevier, vol. 80(19-20), pages 1508-1511, October.
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    Cited by:

    1. Aouicha, Lamia & Messaci, Fatiha, 2019. "Kernel estimation of the conditional density under a censorship model," Statistics & Probability Letters, Elsevier, vol. 145(C), pages 173-180.
    2. Subramanian, Sundarraman, 2021. "Median regression from twice censored data," Statistics & Probability Letters, Elsevier, vol. 168(C).
    3. 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.

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