A Generalized Class of Regression-cum-Ratio Estimators of Population Mean in Simple Random Sampling
The present paper introduces a general class of regression-cum-ratio estimators for the estimation of population mean of the variable under study. The estimators by Kadilar and Cingi (Applied Mathematics and Computation 151:893-902, 2004), and Kadilar and Cingi (Interstat 4:1-11, 2006) are identified as members of the proposed class of estimators. The expression for mean square error (MSE) of the proposed class has been obtained using Taylor series expansion. The proposed class of estimators has been compared with the other well-known estimators using MSE criterion, and the conditions under which the proposed class performs better have been obtained. Theoretical results have been validated
with the help of an empirical study.
Disclaimer/Regarding indexing issue:
We have provided the online access of all issues and papers to the indexing agencies (as given on journal web site). It’s depend on indexing agencies when, how and what manner they can index or not. Hence, we like to inform that on the basis of earlier indexing, we can’t predict the today or future indexing policy of third party (i.e. indexing agencies) as they have right to discontinue any journal at any time without prior information to the journal. So, please neither sends any question nor expects any answer from us on the behalf of third party i.e. indexing agencies.Hence, we will not issue any certificate or letter for indexing issue. Our role is just to provide the online access to them. So we do properly this and one can visit indexing agencies website to get the authentic information. Also: DOI is paid service which provided by a third party. We never mentioned that we go for this for our any journal. However, journal have no objection if author go directly for this paid DOI service.