Optimal generalised Searls estimation procedure of population mean under ranked set sampling scheme

Dinesh K. Sharma and S.K. Yadav

International Journal of Mathematics in Operational Research, 2022, vol. 21, issue 3, 305-320

Abstract: Searls (1964) concluded that some constant multiple of a sample mean is an improved estimator of the study variable's population mean. Further, it is well established that the proper use of auxiliary information elevates the estimator's efficiency. In this study, a novel, more generalised family of Searls type estimators is suggested to enhance the estimation of the main variable's population mean utilising known auxiliary parameters under ranked set sampling (RSS) scheme. The bias and mean squared error (MSE) expressions are studied up to first-order approximation. The optimal values of Searls constants are derived, which minimises the MSE of the introduced estimator. The minimum MSE of the proposed estimator is also obtained. The introduced family of estimators is compared with the competing estimators of the population mean. The efficiency conditions of the proposed class over the competing estimators are also derived. These efficiency conditions are verified through numerical and simulation studies. The comparison is made based on the estimators' MSEs, and the estimator with the least MSE is recommended for practical applications.

Keywords: ranked set sampling; RSS; main variable; auxiliary variable; Searls estimation; bias; mean squared error; MSE; percentage relative efficiency. (search for similar items in EconPapers)
Date: 2022
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=122211 (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:ids:ijmore:v:21:y:2022:i:3:p:305-320

Access Statistics for this article

More articles in International Journal of Mathematics in Operational Research from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().