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November 2015 Nonparametric estimation of the residual entropy function with censored dependent data
G. Rajesh, E. I. Abdul-Sathar, R. Maya, K. R. Muraleedharan Nair
Braz. J. Probab. Stat. 29(4): 866-877 (November 2015). DOI: 10.1214/14-BJPS250

Abstract

The residual entropy function introduced by Ebrahimi [Sankhyā A 58 (1996) 48–56], is viewed as a dynamic measure of uncertainty. This measure finds applications in modeling and analysis of life time data. In the present work, we propose nonparametric estimators for the residual entropy function based on censored data. Asymptotic properties of the estimator are established under suitable regularity conditions. Monte Carlo simulation studies are carried out to compare the performance of the estimators using the mean-squared error. The methods are illustrated using two real data sets.

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G. Rajesh. E. I. Abdul-Sathar. R. Maya. K. R. Muraleedharan Nair. "Nonparametric estimation of the residual entropy function with censored dependent data." Braz. J. Probab. Stat. 29 (4) 866 - 877, November 2015. https://doi.org/10.1214/14-BJPS250

Information

Received: 1 July 2013; Accepted: 1 May 2014; Published: November 2015
First available in Project Euclid: 17 September 2015

zbMATH: 1329.62175
MathSciNet: MR3397397
Digital Object Identifier: 10.1214/14-BJPS250

Rights: Copyright © 2015 Brazilian Statistical Association

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Vol.29 • No. 4 • November 2015
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