In this article, we introduce a dual to ratio-type class of estimators for population mean under simple random sampling using information on auxiliary variable, inspired by the estimator proposed by Srivenkataramana (1980) and Kumar & Siddiqui (2024). Our goal is to develop an efficient class of estimators for population mean. We have derived the bias and mean squared error of the proposed estimator up to the first order of approximation. We have derived the optimum condition at which the suggested class of estimators attained the minimum mean squared error. To assess the performance of the suggested class of estimators,we compare the MSE of our proposed estimator with that of several existing estimators. From the theoretical analysis, we demonstrate that the proposed dual to ratiotype class of estimators exhibits a lower mean squared error compared to some existing alternatives, indicating that it is more efficient. This suggests that the proposed class of estimators provides more accurate estimates for a given sample size. To validate our theoretical results,we have performed a simulation study, which confirms that the proposed estimator consistently outperforms with some existing estimators in terms of efficiency, regardless of whether the study is empirical or simulation based. These findings provide compelling evidence for the effectiveness of the proposed class of estimators in practical applications.
Singh et al. (Thu,) studied this question.