Abstract
This paper presents a class of exponential chain ratio type estimator in double sampling for estimating finite population mean of the study variable, when the information on another additional auxiliary variable is known along with the main auxiliary variable. The property of proposed class of estimator has been studied. Comparison has been made with other competitive estimators. The proposed estimator is found to be more efficient both theoretically and empirically.
Highlights
It is well established fact that the uses of auxiliary information in sample survey increases the precision of the estimate of population mean of study variable
In the ratio method of estimation, it is assumed that the auxiliary information is known in advance
It is evident that the proposed class of exponential chain ratio type estimators t1 is more efficient than the sample mean per unit estimator y, chain
Summary
It is well established fact that the uses of auxiliary information in sample survey increases the precision of the estimate of population mean of study variable. If the population mean Z of another auxiliary variable z , closely related to x but compared to x remotely related to y is known (i.e. ρyx > ρyz ), it is preferable to estimate X by X = x1Z z1 , which would provide better ( ) estimate of X than x1 to the terms of order o n−1 if ρxzCx Cz > 1 2 , where Cx , Cz and ρ yx , ρ yz , ρxz are coefficient of variation of x , z and correlation coefficient between y and x ; y and z ; x and z respectively. Numerical illustrations are given to show the performance of the proposed estimator over other estimators
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Science Journal of Applied Mathematics and Statistics
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
