gamma kernel estimators for density and hazard rate of right-censored data

Clicks: 210
ID: 136891
2011
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Abstract
The nonparametric estimation for the density and hazard rate functions for right-censored data using the kernel smoothing techniques is considered. The “classical” fixed symmetric kernel type estimator of these functions performs well in the interior region, but it suffers from the problem of bias in the boundary region. Here, we propose new estimators based on the gamma kernels for the density and the hazard rate functions. The estimators are free of bias and achieve the optimal rate of convergence in terms of integrated mean squared error. The mean integrated squared error, the asymptotic normality, and the law of iterated logarithm are studied. A comparison of gamma estimators with the local linear estimator for the density function and with hazard rate estimator proposed by Müller and Wang (1994), which are free from boundary bias, is investigated by simulations.
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bouezmarni2011journalgamma Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;T. Bouezmarni;A. El Ghouch;M. Mesfioui
Journal nature protocols
Year 2011
DOI
10.1155/2011/937574
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