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Moderate and large deviation principles for the hazard rate function kernel estimator under censoring

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  • Diallo, Amadou Oury Korbe
  • Louani, Djamal

Abstract

This paper is devoted to pointwise large and moderate deviation principles for the hazard rate function kernel estimator in the right censorship setting. Using the contraction principle and an exponential equivalence, the results are derived as by-products from large and moderate deviation principles stated respectively for processes Zn(x) and Dn(x), defined below.

Suggested Citation

  • Diallo, Amadou Oury Korbe & Louani, Djamal, 2013. "Moderate and large deviation principles for the hazard rate function kernel estimator under censoring," Statistics & Probability Letters, Elsevier, vol. 83(3), pages 735-743.
    • Handle: RePEc:eee:stapro:v:83:y:2013:i:3:p:735-743
    DOI: 10.1016/j.spl.2012.11.010
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    References listed on IDEAS

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    1. repec:adr:anecst:y:2000:i:58:p:09 is not listed on IDEAS
    2. Diehl, Sabine & Stute, Winfried, 1988. "Kernel density and hazard function estimation in the presence of censoring," Journal of Multivariate Analysis, Elsevier, vol. 25(2), pages 299-310, May.
    3. Djamal Louani, 1998. "Large Deviations Limit Theorems for the Kernel Density Estimator," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 25(1), pages 243-253, March.
    4. Cai, Zongwu, 1998. "Kernel Density and Hazard Rate Estimation for Censored Dependent Data," Journal of Multivariate Analysis, Elsevier, vol. 67(1), pages 23-34, October.
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    Cited by:

    1. Liu, Qiaojing & Zhao, Shoujiang, 2013. "Pointwise and uniform moderate deviations for nonparametric regression function estimator on functional data," Statistics & Probability Letters, Elsevier, vol. 83(5), pages 1372-1381.

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