Family Of Enhanced Estimators For Population Mean Using Auxiliary Information Under Simple Random Sampling
Muhammad Atif
Neelam
Hassan Yar Ud Din
Soofia Iftikhar
Muhammad Farooq
Qamruz Zaman
Mujeeb Hussain
Abstract
In this paper, we present an enhanced class of exponential-ratio-type estimators for estimating the finite population mean using simple random sampling without replacement. The proposed estimators depend on the careful and appropriate selection of the parameters of auxiliary information. The inclusion of auxiliary variable decreases the mean square error (MSE) and boosts the accuracy and efficiency. The expressions for bias and mean square error up to order one, along with the theoretical conditions are derived. The results show latest estimators performed better than the prior ones. In order to assess the estimators performance numerically, we use real data set to calculate their mean square error and percentage relative efficiency (PRE). Using numerical results, a comparative study show that the proposed estimators yielded more accurate results than existing ones. Therefore, the proposed family of enhanced estimators can be utilized to achieve better results as compared to the existing mean estimators for the population.