Brazilian Journal of Probability and Statistics

Nonparametric estimation of the residual entropy function with censored dependent data

G. Rajesh, E. I. Abdul-Sathar, R. Maya, and K. R. Muraleedharan Nair

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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.

Article information

Source
Braz. J. Probab. Stat., Volume 29, Number 4 (2015), 866-877.

Dates
Received: July 2013
Accepted: May 2014
First available in Project Euclid: 17 September 2015

Permanent link to this document
https://projecteuclid.org/euclid.bjps/1442513450

Digital Object Identifier
doi:10.1214/14-BJPS250

Mathematical Reviews number (MathSciNet)
MR3397397

Zentralblatt MATH identifier
1329.62175

Keywords
Residual entropy function kernel estimate $\alpha$-mixing residual life

Citation

Rajesh, G.; Abdul-Sathar, E. I.; Maya, R.; Muraleedharan Nair, K. R. Nonparametric estimation of the residual entropy function with censored dependent data. Braz. J. Probab. Stat. 29 (2015), no. 4, 866--877. doi:10.1214/14-BJPS250. https://projecteuclid.org/euclid.bjps/1442513450


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