Generalised classes of estimators for population mean of sensitive variable using non-sensitive auxiliary parameters
by S.K. Yadav; Amit Kumar Misra; Tarushree Bari
International Journal of Mathematical Modelling and Numerical Optimisation (IJMMNO), Vol. 12, No. 3, 2022

Abstract: Sousa et al. (2010) suggested transformed ratio type estimators for estimating the population mean of a sensitive variable in presence of some known population coefficients of a non-sensitive supplementary variable. In this article, we generalise the Sousa et al. (2010) family of estimators using some new population parameters of auxiliary information based on a randomised response technique (RRT). Further, we introduce a new efficient family of estimators for estimating the population mean of sensitivity variable using the approach given in Searls (1964) in the presence of the auxiliary information. The optimal value of Searl's constant is obtained using Lagrange's method of maxima-minima. Theoretical results are supported with a numerical illustration based on real datasets. In addition, a simulation study is carried out to compare the performances of the suggested and competing families of estimators. The estimator with good sampling properties and a lower mean square error (MSE) is recommended for various fields of applications of sensitive research.

Online publication date: Tue, 05-Jul-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 Mathematical Modelling and Numerical Optimisation (IJMMNO):
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