An improved estimator of population mean under PPS sampling with application to radiation data sets

In many fields of sciences, research studies require estimation of the mean of different variables such as temperature, air quality, airflight time, and tensile strength, etc. The traditional equal probability sampling designs are used when all of the population units have equal importance. In many real-world situations, the relative importance of population units varies from unit to unit. In such cases, probabilities proportional to size (PPS) sampling is employed for sample selection. This paper introduces a novel estimator for estimating the population mean utilizing an auxiliary variable. The mathematical properties of the suggested mean estimator have been analyzed and compared with other competitor mean estimators. Two different data sets on food irradiation have been used for performance comparison between the proposed and competitor estimators. The results reveal that the new suggested mean estimator performs more precisely than the competitor estimators which make it suitable for applications in real-world estimation problems.