Imputation techniques utilizing higher order moment of a supplementary variable under simple random sampling

This paper presents certain log-based categories of imputation techniques utilizing higher order moment of a supplementary variable under simple random sampling. The pertinent categories of point estimators have been developed to estimate the population mean. Bias and MSE expressions are derived up to the approximation of first order. The performance of suggested estimators are investigated in relation to the estimators proposed by Bahl and Tuteja [2], Izunobi and Onyeka [10] and Zaman and Iftikhar [28]. The comparative analysis shows that the suggested estimators outperform those suggested by Bahl and Tuteja [2], Izunobi and Onyeka [10] and Zaman and Iftikhar [28]. The theoretical results are supported by a numerical study on two real populations as well as a simulation study using a hypothetical population. A simulation study also reveals that the suggested estimators performed superior at high correlation than at low correlation.