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A new proof of strong consistency of kernel estimation of density function and mode under random censorship

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  • Gannoun, Ali
  • Saracco, Jérôme

Abstract

In this paper, we establish a new proof of uniform consistency of kernel estimator of density function when we observe a random right censored model. This proof uses an exponential inequality established by Wang (2000). As a consequence, we obtain the almost sure convergence of the kernel estimator of the mode.

Suggested Citation

  • Gannoun, Ali & Saracco, Jérôme, 2002. "A new proof of strong consistency of kernel estimation of density function and mode under random censorship," Statistics & Probability Letters, Elsevier, vol. 59(1), pages 61-66, August.
    • Handle: RePEc:eee:stapro:v:59:y:2002:i:1:p:61-66
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    References listed on IDEAS

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    1. Chaubey Yogendra P. & Sen Pranab K., 1996. "On Smooth Estimation Of Survival And Density Functions," Statistics & Risk Modeling, De Gruyter, vol. 14(1), pages 1-22, January.
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    Cited by:

    1. Bezandry, Paul H. & Bonney, George E. & Gannoun, Ali, 2005. "Consistent estimation of the density and hazard rate functions for censored data via the wavelet method," Statistics & Probability Letters, Elsevier, vol. 74(4), pages 366-372, October.

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