A Novel Estimator for Finite Population Mean in the Presence of Minimum and Maximum Values

Abstract

The goal of survey sampling theory is to produce reliable and precise estimates for population parameters. To achieve this, a new estimator for finite population mean that incorporates dual auxiliary variables in the presence of minimum and maximum values is proposed in this study. Theoretical derivations and empirical evaluations demonstrate the superiority of the proposed estimator over existing alternatives, as it consistently yields lower mean squared errors and biases. While its performance improves with larger sample sizes, it also maintains strong efficiency in small-sample settings.