A generalized family of estimators for estimating finite population mean in circular systematic sampling (CSS)

Housila P. Singh, Anita Yadav and Swarangi M. Gorey

Communications in Statistics - Theory and Methods, 2021, vol. 50, issue 3, 631-662

Abstract: This paper aims to discuss the problem of estimating the population mean Y¯ of the study variable y using information on an auxiliary variable x in circular systematic sampling (CSS) scheme along with the non-response problem. We have suggested a generalized family of estimators for population mean Y¯ of y based on auxiliary variable x. We have derived the expressions for bias and mean squared error of the proposed family of estimators upto first order of approximation. Optimum conditions are obtained in which the suggested family of estimators has the least mean squared error. It has been shown that the proposed family of estimators is better than usual unbiased estimator, ratio estimator and the regression estimator. An empirical study is provided to show that the suggested family of estimators is better than the ratio and regression estimators.

Date: 2021
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/03610926.2019.1639750 (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:3:p:631-662

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

DOI: 10.1080/03610926.2019.1639750

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 ().