Double Exponential Ratio Estimator of a Finite Population Variance under Extreme Values in Simple Random Sampling

Umer Daraz, Jinbiao Wu () and Olayan Albalawi
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Umer Daraz: School of Mathematics and Statistics, Central South University, Changsha 410017, China
Jinbiao Wu: School of Mathematics and Statistics, Central South University, Changsha 410017, China
Olayan Albalawi: Department of Statistics, Faculty of Science, University of Tabuk, Tabuk 71491, Saudi Arabia

Mathematics, 2024, vol. 12, issue 11, 1-11

Abstract: This article presents an improved class of efficient estimators aimed at estimating the finite population variance of the study variable. These estimators are especially useful when we have information about the minimum/maximum values of the auxiliary variable within a framework of simple random sampling. The characteristics of the proposed class of estimators, including bias and mean squared error ( M S E ) under simple random sampling are derived through a first-order approximation. To assess the performance and validate the theoretical outcomes, we conduct a simulation study. Results indicate that the proposed class of estimators has lower M S E s as compared to other existing estimators across all simulation scenarios. Three datasets are used in the application section to emphasize the effectiveness of the proposed class of estimators over conventional unbiased variance estimators, ratio and regression estimators, and other existing estimators.

Keywords: auxiliary information; study variable; minimum and maximum values; variance estimation; bias; MSE (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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