On Enhanced Ratio-Type Estimators Using Quantile Regression for Finite Population Mean under Robustness and Empirical Validation

Abstract

When the conditions of traditional regression analysis aren't met, an alternative method called quantile regression is utilized to estimate the value of the study variable across different quantiles of the distribution. This study proposes leveraging quantile regression information to develop ratio-type estimators for the finite population mean, particularly under robust measures of auxiliary variables in simple random sampling (SRS) without replacement. The performance of these proposed families of estimators is compared with existing studies using metrics such as mean squared error (MSE) equations and percentage relative efficiency (PRE). Additionally, this article incorporates simulation studies. Moreover, various real-world datasets are considered for empirical investigation to validate the theoretical findings.