A novel and efficient estimation of population mean using auxiliary information under simple random sampling in sample surveys

This study proposes improved classes of estimators that efficiently incorporate transformed auxiliary information to enhance population mean estimation under simple random sampling. By minimizing the mean square error under optimal conditions, the proposed estimators outperform existing estimators. The superiority is validated through theoretical analysis, simulations and real data ensuring greater precision and reliability in survey estimation. The properties of the estimators are investigated up to the first order of approximation. Optimum conditions are obtained under which suggested estimators have the least mean square error. Theoretical comparison of the proposed estimators with some existing estimators has been made. The proposed study is supported by numerical illustrations based on simulation studies and real data sets. On the basis of simulation and empirical studies, suitable suggestions have been made for the effective application of proposed classes of estimators for estimating mean.