scholarly journals Adjustment of the auxiliary variable(s) for estimation of a finite population mean

2011 ◽  
Author(s):  
Ayesha Nazuk ◽  
Sadia Nadir ◽  
Javid Shabbir
Keyword(s):  
Finite Population ◽  
Auxiliary Variable ◽  
Population Mean

scholarly journals An Alternative Estimator for Estimating the Finite Population Mean Using Auxiliary Information in Sample Surveys

2012 ◽  
Vol 2012 ◽  
pp. 1-14 ◽  
Author(s):  
Ramkrishna S. Solanki ◽  
Housila P. Singh ◽  
Anjana Rathour
Keyword(s):  
Finite Population ◽  
Auxiliary Information ◽  
Auxiliary Variable ◽  
Population Mean ◽  
Asymptotic Expressions ◽  
Variance Formula ◽  
Optimum Estimator ◽  
Class Of Estimators ◽  
Alternative Estimator ◽  
Usual Product

This paper suggests a class of estimators for estimating the finite population mean of the study variable using known population mean of the auxiliary variable . Asymptotic expressions of bias and variance of the suggested class of estimators have been obtained. Asymptotic optimum estimator (AOE) in the class is identified along with its variance formula. It has been shown that the proposed class of estimators is more efficient than usual unbiased, usual ratio, usual product, Bahl and Tuteja (1991), and Kadilar and Cingi (2003) estimators under some realistic conditions. An empirical study is carried out to judge the merits of suggested estimator over other competitors practically.

A MODIFIED RATIO TYPE ESTIMATOR OF FINITE POPULATION MEAN UNDER STRATIFIED RANDOM SAMPLING SCHEME

2021 ◽  
pp. 58-60
Author(s):  
Naziru Fadisanku Haruna ◽  
Ran Vijay Kumar Singh ◽  
Samsudeen Dahiru
Keyword(s):  
Mean Square Error ◽  
Random Sampling ◽  
Minimum Mean Square Error ◽  
Real Life ◽  
Population Data ◽  
Auxiliary Variable ◽  
Mean Square ◽  
Data Set ◽  
Population Mean ◽  
The Mean

In This paper a modied ratio-type estimator for nite population mean under stratied random sampling using single auxiliary variable has been proposed. The expression for mean square error and bias of the proposed estimator are derived up to the rst order of approximation. The expression for minimum mean square error of proposed estimator is also obtained. The mean square error the proposed estimator is compared with other existing estimators theoretically and condition are obtained under which proposed estimator performed better. A real life population data set has been considered to compare the efciency of the proposed estimator numerically.

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