Estimation of population size in entropic perspective
Zhiyi Zhang,
Chen Chen and
Jialin Zhang
Communications in Statistics - Theory and Methods, 2020, vol. 49, issue 2, 307-324
Abstract:
Nonparametric estimation of population size is a long standing and difficult problem. It is difficult because, particularly from a likelihood perspective, the underlying distribution could vary greatly and many small probability events may not be observed in a sample. However if approached from an entropic standpoint, certain trends can be exploited. This article proposes several estimators based on an entropic representation of population size, and establishes their consistency. Simulation results of the proposed estimators are also reported in comparison with a well-known estimator, and the advantages are noted. Two examples with real data are also included.
Date: 2020
References: Add references at CitEc
Citations: View citations in EconPapers (2)
Downloads: (external link)
http://hdl.handle.net/10.1080/03610926.2018.1536786 (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:49:y:2020:i:2:p:307-324
Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/lsta20
DOI: 10.1080/03610926.2018.1536786
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 ().