Exploring the dependability of Combined Ratio Estimators in Stratified Ranked Set Sampling: Insights from COVID-19 data

Abstract

In this study, we introduce a novel combined ratio type estimator within the framework of Stratified Ranked Set Sampling to estimate the population mean of the study variable by incorporating bivariate auxiliary information. We conduct a comprehensive comparative analysis, including traditional combined ratio, combined regression, Shabbir and Khan [13], and Bhushan and Kumar [37] estimators. We assess the bias and mean squared error of the proposed estimator under the initial degree of approximation. The data source consists of COVID-19 data up to July 2023. Through empirical investigation and simulation studies, our proposed estimator consistently demonstrates superior performance compared to its counterparts, exhibiting the highest relative efficiency. These findings underscore the practical significance of our research in public health and decision-making, emphasizing the potential of this estimator to provide more accurate and reliable estimates in various applications involving ranked set sampling and auxiliary information.

Keywords

• Ranked Set Sampling
• Mean squared error
• Bias
• Stratified Ranked Set Sampling
• Auxiliary variable
• Study variable