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Nonparametric local linear estimation of the relative error regression function for twice censored data

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  • Feriel, Bouhadjera
  • Elias, Ould Saïd

Abstract

This paper deals with the problem of nonparametric relative error regression function estimation for twice censored data. A new estimate is proposed, which is built by combining the local linear approach and relative error estimation. A uniform almost sure consistency with rate over a compact set is established. A numerical study is carried out to assess the performance of the proposed estimator. Practical results indicate the robustness of the new estimate compared to other existing estimators in the presence of censored and outliers datum.

Suggested Citation

  • Feriel, Bouhadjera & Elias, Ould Saïd, 2021. "Nonparametric local linear estimation of the relative error regression function for twice censored data," Statistics & Probability Letters, Elsevier, vol. 178(C).
    • Handle: RePEc:eee:stapro:v:178:y:2021:i:c:s0167715221001474
    DOI: 10.1016/j.spl.2021.109185
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    References listed on IDEAS

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    1. Messaci, Fatiha & Nemouchi, Nahima, 2011. "A law of the iterated logarithm for the product limit estimator with doubly censored data," Statistics & Probability Letters, Elsevier, vol. 81(8), pages 1241-1244, August.
    2. Park, Heungsun & Stefanski, L. A., 1998. "Relative-error prediction," Statistics & Probability Letters, Elsevier, vol. 40(3), pages 227-236, October.
    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.
    4. Chen, Kani & Guo, Shaojun & Lin, Yuanyuan & Ying, Zhiliang, 2010. "Least Absolute Relative Error Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 105(491), pages 1104-1112.
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