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

Javid Shabbir,Ronald Onyango,Aamir Sanaullah,Shakeel Ahmed,Ishfaq Ahmad

doi:10.1155/2021/5245621

Abstract

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.

Highlights

Ratio, product, exponential-ratio, logratio, and regression type estimators are modified or constructed by many researchers to enhance the precision of the estimators under different sampling designs by using the auxiliary variables. ese estimators are commonly used by taking the advantage of correlation coefficient between the study variable and the auxiliary variable(s)
We propose a ratio-exponential-log type general class of estimators in estimating the finite population mean using two auxiliary variables when some parameters of the auxiliary variables are known
We have proposed ratio-exponential-log type generalized class of estimators by combing a ratio, exponential-ratio, and log-ratio type estimators by using the linear transformation for finite population mean in simple random sampling

Summary

Introduction

Ratio, product, exponential-ratio, logratio, and regression type estimators are modified or constructed by many researchers to enhance the precision of the estimators under different sampling designs by using the auxiliary variables. ese estimators are commonly used by taking the advantage of correlation coefficient between the study variable and the auxiliary variable(s).

Some Existing Estimators

Proposed General Class of Estimators

Numerical Example

Comparison of Estimators

Conclusion