An improved ratio-cum-exponential estimator for finite population mean with properties: Applications to radiation monitoring and economic management data
5 affiliations
- 1School of Economics and Management, Jiujiang Polytechnic University of Science and Technology, Jiujiang 332020, Jiangxi, China; Corresponding author.
- 2School of Architectural and Hydraulic Engineering, Jiujiang Polytechnic, University of Science and Technology, Jiujiang 332020, Jiangxi, China
- 3Department of Mathematical Science, College of Science, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
- 4Department of Mathematics, College of Science, Qassim University, Buraydah 51452, Saudi Arabia
- 5Department of Operations and Management Research, Faculty of Graduate Studies for Statistical Research, Cairo University, Giza 12613, Egypt
Abstract
In simple random sampling without replacement (SRSWOR), auxiliary information is an important component for improving the precision of estimators of finite population means. Although a few estimators have been developed to combine dual auxiliary information using a ratio-cum-exponential structure, most estimators rely on a single auxiliary variable, either through ratio, product, or regression structures. This study presents a new ratio/cu-exponential estimator for estimation of the finite population mean by combining two auxiliary variables in an optimized exponential structure. We determine the bias and mean squared error (MSE) of the proposed estimator using a first-order approximation method, and we determine the optimal values of the characterizing constants analytically. We formulate conditions for theoretical efficiency to compare the proposed estimator with conventional estimators such as the sample mean, ratio, product, regression, exponential, and existing dual auxiliary estimators. We also use a real dataset from a radiation monitoring and economic management application to evaluate the proposed estimator’s performance in terms of MSE and PRE. The results show that the proposed estimator has uniformly lower MSE and PRE than the other estimators across various correlation structures and population characteristics. The proposed ratio-cum-exponential estimator has been verified to be a powerful and efficient method for estimating finite population means in various scientific, physical, and socio-economic settings.
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BibTeX
@article{cai2026improved,
title = {{An improved ratio-cum-exponential estimator for finite population mean with properties: Applications to radiation monitoring and economic management data}},
author = {Aiting Cai and Jingwen Li and Shahah Almutairi and Mona Almutairi and Alhanouf Alburaikan and Hamiden Abd El-Wahed Khalifa},
journal = {Journal of Radiation Research and Applied Sciences},
year = {2026},
month = oct,
doi = {10.1016/j.jrras.2026.102746},
url = {https://doi.org/10.1016/j.jrras.2026.102746},
note = {CC BY},
}
RIS
TY - JOUR
TI - An improved ratio-cum-exponential estimator for finite population mean with properties: Applications to radiation monitoring and economic management data
AU - Aiting Cai
AU - Jingwen Li
AU - Shahah Almutairi
AU - Mona Almutairi
AU - Alhanouf Alburaikan
AU - Hamiden Abd El-Wahed Khalifa
PY - 2026
DA - 2026/10/30
JO - Journal of Radiation Research and Applied Sciences
DO - 10.1016/j.jrras.2026.102746
UR - https://doi.org/10.1016/j.jrras.2026.102746
AB - In simple random sampling without replacement (SRSWOR), auxiliary information is an important component for improving the precision of estimators of finite population means. Although a few estimators have been developed to combine dual auxiliary information using a ratio-cum-exponential structure, most estimators rely on a single auxiliary variable, either through ratio, product, or regression structures. This study presents a new ratio/cu-exponential estimator for estimation of the finite population mean by combining two auxiliary variables in an optimized exponential structure. We determine the bias and mean squared error (MSE) of the proposed estimator using a first-order approximation method, and we determine the optimal values of the characterizing constants analytically. We formulate conditions for theoretical efficiency to compare the proposed estimator with conventional estimators such as the sample mean, ratio, product, regression, exponential, and existing dual auxiliary estimators. We also use a real dataset from a radiation monitoring and economic management application to evaluate the proposed estimator’s performance in terms of MSE and PRE. The results show that the proposed estimator has uniformly lower MSE and PRE than the other estimators across various correlation structures and population characteristics. The proposed ratio-cum-exponential estimator has been verified to be a powerful and efficient method for estimating finite population means in various scientific, physical, and socio-economic settings.
SN - 2314-7164
SN - 1687-8507
ER -