Estimation of entropy and extropy based on right censored data: A Bayesian non-parametric approach-Reference-Cited by-同舟云学术

Estimation of entropy and extropy based on right censored data: A Bayesian non-parametric approach

Published:2022-09-30 Issue:4 Volume:28 Page:319-328
ISSN:0929-9629
Container-title:Monte Carlo Methods and Applications
language:en
Short-container-title:

Author:

Al-Labadi Luai¹,Tahir Muhammad¹

Affiliation:

1. Department of Mathematical and Computational Sciences , University of Toronto Mississauga , Mississauga , Ontario L5L 1C6 , Canada

Abstract

Abstract Entropy and extropy are central measures in information theory. In this paper, Bayesian non-parametric estimators to entropy and extropy with possibly right censored data are proposed. The approach uses the beta-Stacy process and the difference operator. Examples are presented to illustrate the performance of the estimators.

Publisher

Walter de Gruyter GmbH

Subject

Applied Mathematics,Statistics and Probability

Link

https://www.degruyter.com/document/doi/10.1515/mcma-2022-2123/pdf

Reference34 articles.

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5. L. Al Labadi and M. Zarepour, A Bayesian nonparametric goodness of fit test for right censored data based on approximate samples from the beta-Stacy process, Canad. J. Statist. 41 (2013), no. 3, 466–487.

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