A generalized class of estimators for the mean using multiauxiliary information in adaptive cluster sampling

Housila P. Singh, Anurag Gupta, Rajesh Tailor and Neha Garg

Communications in Statistics - Theory and Methods, 2024, vol. 53, issue 22, 7871-7892

Abstract: This article addresses the problem of estimating the population mean of variable y using information from multiple auxiliary variables in adaptive cluster sampling. We define a class of estimators based on p(>1) auxiliary variables x1,x2,…..,xp, derive bias and mean squared error expressions up to order n−1, and determine optimal conditions for minimizing mean squared error. Our findings demonstrate that the proposed estimators outperform the multiple linear regression estimator. We also identify several existing estimators as members of this class, including those introduced by Dryver and Chao (2007), Chutiman and Kumphon (2008), Chutiman (2013), Chaudhry (2014), Chaudhry and Hanif (2015), and Yadav et al. (2016). Furthermore, we obtain the correct values of constants “a” and “b” for the Chaudhry and Hanif (2015) estimator, leading to an accurate expression for its minimum mean squared error. To assess the performance of these estimators, we conduct a simulation study.

Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/03610926.2023.2274809 (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:53:y:2024:i:22:p:7871-7892

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

DOI: 10.1080/03610926.2023.2274809

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 ().