Some efficient classes of estimators under stratified sampling

Shashi Bhushan, Anoop Kumar and Saurabh Singh

Communications in Statistics - Theory and Methods, 2023, vol. 52, issue 6, 1767-1796

Abstract: The essence of this paper is to propose some efficient combined and separate classes of estimators for estimating population mean Y¯ under stratified simple random sampling. The bias and mean square error of the proposed classes of estimators are obtained. The proposed estimators are theoretically justified over the conventional mean estimator, classical ratio and regression estimators, Kadilar and Cingi estimators, Shabbir and Gupta estimators, Singh and Vishwakarma estimators, Koyuncu and Kadilar estimators, Singh and Solanki estimator, Yadav et al. estimator, Solanki and Singh estimator and Shahzad et al. estimator. The theoretical findings are extended with a simulation study accomplished over some artificially generated symmetric and asymmetric populations.

Date: 2023
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/03610926.2021.1939052 (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:52:y:2023:i:6:p:1767-1796

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

DOI: 10.1080/03610926.2021.1939052

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 ().