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Efficient estimation of a distribution function based on censored data

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  • Alevizos, Filippos
  • Bagkavos, Dimitrios
  • Ioannides, Dimitrios

Abstract

The asymptotic relative deficiency of the Kaplan–Meier over an existing kernel estimate is established. Further, finite sample numerical evidence is provided suggesting that the kernel estimate is preferable when performance is measured through the mean square error.

Suggested Citation

  • Alevizos, Filippos & Bagkavos, Dimitrios & Ioannides, Dimitrios, 2019. "Efficient estimation of a distribution function based on censored data," Statistics & Probability Letters, Elsevier, vol. 145(C), pages 359-364.
    • Handle: RePEc:eee:stapro:v:145:y:2019:i:c:p:359-364
    DOI: 10.1016/j.spl.2018.09.003
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    References listed on IDEAS

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    1. J. Ghorai & V. Susarla, 1990. "Kernel estimation of a smooth distribution function based on censored data," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 37(1), pages 71-86, December.
    2. Zongwu Cai & George G. Roussas, 1998. "Efficient Estimation of a Distribution Function under Quadrant Dependence," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 25(1), pages 211-224, March.
    3. M. Falk, 1983. "Relative efficiency and deficiency of kernel type estimators of smooth distribution functions," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 37(2), pages 73-83, June.
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