Ratio estimators based on a ranked set sample in a finite population setting

2018 ◽  
Vol 47 (2) ◽  
pp. 226-238 ◽  
Author(s):  
Omer Ozturk
Keyword(s):  
Finite Population ◽  
Ranked Set Sample ◽  
Ratio Estimators

scholarly journals Ameliorated Class of Estimators of Finite Population Variance

2020 ◽  
pp. 1-14
Author(s):  
J. O. Muili ◽  
E. N. Agwamba ◽  
A. B. Odeyale ◽  
A. Adebiyi
Keyword(s):  
Finite Population ◽  
Population Variance ◽  
First Order ◽  
Class Of Estimators ◽  
The Mean ◽  
Ratio Estimators ◽  
Mean Square Errors ◽  
Theoretical Results ◽  
Percentage Relative Efficiency ◽  
Taylor’S Series

A class of ratio estimators of finite population variance is proposed in this study. The properties of the proposed estimators have been derived using Taylor’s Series method up to first order of approximation. The efficiency conditions which are the mean square errors (MSEs) and percentage relative efficiency (PRE) of the proposed estimators over existing estimators have been established. The analytical illustration was also conducted to affirm the theoretical results. The results of the empirical study revealed that the proposed estimators are more efficient than the existing estimators considered in the study.

scholarly journals Measuring Performance of Ratio-Exponential-Log Type General Class of Estimators Using Two Auxiliary Variables

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Javid Shabbir ◽  
Shakeel Ahmed ◽  
Aamir Sanaullah ◽  
Ronald Onyango
Keyword(s):  
General Class ◽  
Finite Population ◽  
Real Life ◽  
Auxiliary Variables ◽  
First Order ◽  
Class Of Estimators ◽  
The Mean ◽  
Ratio Estimators ◽  
Efficiency Comparison ◽  
Log Ratio

In this paper, a ratio-exponential-log type general class of estimators is proposed in estimating the finite population mean using two auxiliary variables when population parameters of the auxiliary variables are known. From the proposed estimator, some special estimators are identified as members of the proposed general class of estimators. The mean square error (MSE) expressions are obtained up to the first order of approximation. This study finds that the proposed general class of estimators outperforms as compared to the conventional mean estimator, usual ratio estimators, exponential-ratio estimators, log-ratio type estimators, and many other competitor regression type estimators. Four real-life applications are used for efficiency comparison.

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