Logarithmic imputation methods under correlated measurement errors

In sample survey, missing data is a common issue. Various imputation techniques have been developed to handle the missing data issue. But, a miniscule work has been done to handle missing data issue in the presence of measurement errors (ME) and correlated measurement errors (CME). This manuscript proposes a few logarithmic imputation techniques and the accompanying point estimators to address the missing data issue when the data are affected by CME. The mean square error (MSE) of the proposed imputation methods is reported to the first order approximation. The dominance conditions of the proposed imputation methods over the coeval imputation methods are obtained. Afterward, a simulation study using an artificially drawn population and a real data application are carried out to support the theoretical findings.