EconPapers    
Economics at your fingertips  
 

A note on the strong consistency of nonparametric estimation of Shannon entropy in length-biased sampling

Farzaneh Oliazadeh, Anis Iranmanesh and Vahid Fakoor

Communications in Statistics - Theory and Methods, 2021, vol. 50, issue 24, 5779-5791

Abstract: In this article, we propose an estimator of Shannon entropy based on kernel estimators of density function in length-biased setting. The strong consistency of the proposed estimators is established. In addition, some simulations are conducted to evaluate the performance of the proposed estimator.

Date: 2021
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/03610926.2020.1737123 (text/html)
Access to full text is restricted to subscribers.

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:taf:lstaxx:v:50:y:2021:i:24:p:5779-5791

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/lsta20

DOI: 10.1080/03610926.2020.1737123

Access Statistics for this article

Communications in Statistics - Theory and Methods is currently edited by Debbie Iscoe

More articles in Communications in Statistics - Theory and Methods from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().

 
Page updated 2025-03-20
Handle: RePEc:taf:lstaxx:v:50:y:2021:i:24:p:5779-5791