Burg entropy in terms of survival function and its application in model selection

Omid Kharazmi (), Shital Saha () and Suchandan Kayal ()
Additional contact information
Omid Kharazmi: Vali-e-Asr University of Rafsanjan
Shital Saha: Dayananda Sagar University
Suchandan Kayal: National Institute of Technology Rourkela

Methodology and Computing in Applied Probability, 2025, vol. 27, issue 4, 1-20

Abstract: Abstract In this paper, we introduce the cumulative residual Burg entropy as a survival-based extension of the classical Burg entropy. Consequently, we define a relative version of this measure and a Jensen–cumulative residual Burg entropy divergence to quantify dispersion between two survival functions. We derive key properties and bounds for the proposed entropy under proportional survival functions and establish its equivalence with proportional hazard structures. Furthermore, the proposed entropy and its relative divergence are explored for geometric and harmonic mixture survival models. A nonparametric estimator for the cumulative residual Burg entropy is proposed, and its convergence properties are examined. Finally, we present an application demonstrating the utility of the relative cumulative residual Burg divergence as a model selection criterion using a real dataset on water capacities of the Shasta Reservoir in California.

Keywords: Cumulative residual entropy; Cumulative residual Fisher information; Burg entropy; Jensen inequality; Model selection; 94A15; 62F86; 60E05 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s11009-025-10208-z Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:metcap:v:27:y:2025:i:4:d:10.1007_s11009-025-10208-z

Ordering information: This journal article can be ordered from
https://www.springer.com/journal/11009

DOI: 10.1007/s11009-025-10208-z

Access Statistics for this article

Methodology and Computing in Applied Probability is currently edited by Joseph Glaz

More articles in Methodology and Computing in Applied Probability from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().