Generalized Ratio-Cum-Exponential-Log Ratio Type Estimators of Population Mean under Simple Random Sampling Scheme

This paper aims to discuss the improvement of the estimation of the finite population mean of the study variable by employing an auxiliary variable in a simple random sampling scheme. We propose a new general ratio type estimator consisting of both exponential and logarithm functions. The sampling characteristics, Bias and Mean Squared Error (MSE) of the proposed estimator are evaluated up to first order approximation. The optimum values for the characterizing constants are observed. For the optimum values of the characterizing scalars, the minimum value of the MSE of the proposed estimator is also obtained. The proposed estimator is theoretically compared to the competing population mean estimators. The efficiency conditions for the proposed estimator to outperform the competing estimators are also obtained. Real and simulated data sets are used to validate the theoretical requirements for the proposed estimator's inherent superiority over rival estimators.