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Nonparametric robust regression estimation for censored data

Author

Listed:
  • Mohamed Lemdani

    (Univ. de Lille 2.)

  • Elias Ould Saïd

    (Univ. Lille Nord de France
    Univ. du Littoral Côte d’Opale, LMPA)

Abstract

In this paper, we consider a robust regression estimator when the interest random variable is subject to random right-censoring. Based on the so-called synthetic data, we define a new kernel estimator. Under classical conditions and using a VC-classes theory, we establish its uniform consistency with rate and asymptotic normality properties. Special cases are studied and simulations are drawn to illustrate the main results.

Suggested Citation

  • Mohamed Lemdani & Elias Ould Saïd, 2017. "Nonparametric robust regression estimation for censored data," Statistical Papers, Springer, vol. 58(2), pages 505-525, June.
    • Handle: RePEc:spr:stpapr:v:58:y:2017:i:2:d:10.1007_s00362-015-0709-8
    DOI: 10.1007/s00362-015-0709-8
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    References listed on IDEAS

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