Work

Regression cum exponential type estimator for population proportion using auxiliary attributes: Application on radiation data and simulation study

Year: 2025

Type: article

Abstract: In numerous instances, incorporating an auxiliary attributes enhances the precision of population parameter estimate. This study enhances the knowledge of survey sampling by offering an improved estimator for determining the population proportion in simple random sampling that incorporates an auxiliary attribute. We find out the expression of the bias and mean square error (MSEs) for both the existing and new estimators up to the first order of approximation. We determined the theoretical optimum values and then computed the least MSE of the estimator that was initially recommended. The performance of the estimators was evaluated using three real-world datasets from the radiation sciences, and their reliability was confirmed by a simulation analysis. An empirical study showed that the proposed estimator has a greater percentage relative efficiency PREs than the considered existing estimators. In light of this, it may be important to investigate the proposed estimator's dominating characteristics of its practical applications.

Source: Journal of Radiation Research and Applied Sciences

DOI: 10.1016/j.jrras.2025.101479

Authors: Badr Aloraini, L.S. Diab, Sohaib Ahmad

Institutions: Shaqra University, Imam Mohammad ibn Saud Islamic University, Abdul Wali Khan University Mardan

Language: English

Topic: Survey Sampling and Estimation Techniques

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,400

Funders: Deanship of Scientific Research, Imam Mohammed Ibn Saud Islamic University, Al-Imam Muhammad Ibn Saud Islamic University