Searls estimation strategy for population mean of a sensitive study variable harnessing non-sensitive auxiliary information
by S.K. Yadav; Amit Kumar Misra; Tarushree Bari
International Journal of Mathematics in Operational Research (IJMOR), Vol. 23, No. 3, 2022

Abstract: In this study, we present a Searls type regression estimator for elevated estimation of the population mean of a sensitive study variable in the presence of a known non-sensitive supplementary variable under the simple random sampling scheme. The first order of approximation is used to obtain the bias and mean square error expressions. The suggested family of estimators is compared to competing estimators both theoretically and numerically. The findings verified through the real and simulated data show that the suggested estimator is preferably chosen over many of the existing competing estimators.

Online publication date: Thu, 01-Dec-2022

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Mathematics in Operational Research (IJMOR):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com