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A New Smooth Density Estimator for Non-Negative Random Variables


A New Smooth Density Estimator for Non-Negative Random Variables

Chaubey, Yogendra P., Sen, Arusharka and Sen, Pranab K. (2007) A New Smooth Density Estimator for Non-Negative Random Variables. Technical Report. Concordia University. Department of Mathematics & Statistics, Montreal, Quebec.

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Commonly used kernel density estimators may not provide admissible values of the density or its functionals at the boundaries for densities with restricted support. For smoothing the empirical distribution a generalization of the Hille's lemma, considered here, alleviates some of the problems of kernel density estimator near the boundaries. For nonnegative random variables which crop up in reliability and survival analysis, the proposed procedure is
thoroughly explored; its consistency and asymptotic distributional results are established under appropriate regularity assumptions. Methods of obtaining smoothing parameters
through cross-validation are given, and graphical illustrations of the estimator for continuous
(at zero) as well as discontinuous densities are provided.

Divisions:Concordia University > Faculty of Arts and Science > Mathematics and Statistics
Item Type:Monograph (Technical Report)
Authors:Chaubey, Yogendra P. and Sen, Arusharka and Sen, Pranab K.
Series Name:Department of Mathematics & Statistics. Technical Report No. 1/07
Corporate Authors:Concordia University. Department of Mathematics & Statistics
Institution:Concordia University
Date:January 2007
Keywords:Asymptotics; boundary correction; cross-validation; empirical distribution; hazard function; Hille's lemma; kernel density estimator; survival function
ID Code:6681
Deposited On:03 Jun 2010 20:45
Last Modified:18 Jan 2018 17:29


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