A New Exponential Ratio-Type Estimator for Population Mean Using Two Auxiliary Variables in Double Sampling

In this study, a new exponential ratio-type estimator for estimating the population mean using two auxiliary variables in double sampling is proposed. The bias and mean squared error (MSE) of the proposed estimator are derived. The efficiency of the proposed estimator is compared theoretically with some existing estimators such as the usual sample mean estimator, the classical ratio estimator, and other exponential estimators. An empirical study using real-life data is conducted to examine the performance of the estimator. The results indicate that the proposed estimator has the smallest mean square error and the highest relative efficiency among the estimators considered. Hence, the estimator is recommended for practical applications in survey sampling where auxiliary information is available.

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Publication Details

Journal
Iconic Research and Engineering Journals
Published
2026-03-23
DOI
https://doi.org/10.64388/irev9i9-1715360
Primary Topic
Survey Sampling and Estimation Techniques
Type
article
Field-Weighted Citation Impact
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article

A New Exponential Ratio-Type Estimator for Population Mean Using Two Auxiliary Variables in Double Sampling

Victor Oluwatobi Abifade, A. Oyinloye, Oluwasola Joshua Ayodele, Olanrewaju Faweya et al.
Iconic Research and Engineering Journals
Survey Sampling and Estimation Techniques
article

A New Exponential Ratio-Type Estimator for Population Mean Using Two Auxiliary Variables in Double Sampling

Victor Oluwatobi Abifade, A. Oyinloye, Oluwasola Joshua Ayodele, Olanrewaju Faweya, Oluwadare Akinyemi
article en

Abstract

In this study, a new exponential ratio-type estimator for estimating the population mean using two auxiliary variables in double sampling is proposed. The bias and mean squared error (MSE) of the proposed estimator are derived. The efficiency of the proposed estimator is compared theoretically with some existing estimators such as the usual sample mean estimator, the classical ratio estimator, and other exponential estimators. An empirical study using real-life data is conducted to examine the performance of the estimator. The results indicate that the proposed estimator has the smallest mean square error and the highest relative efficiency among the estimators considered. Hence, the estimator is recommended for practical applications in survey sampling where auxiliary information is available.

Iconic Research and Engineering JournalsVol. 9(9)
Openalex Percentile: Top 18%
Survey Sampling and Estimation Techniques
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