Improved estimators for mean estimation in presence of missing information

The treatment of incomplete data is an important step in statistical data analysis of most survey datasets. Missing values creates a boisterous situation for the survey researchers in producing the precise estimate of the desired population parameters. To handle these situations, imputation methods play a significant role in filling incomplete response values when it is necessary to use information on complete sampled units and not to discard the data with missingness. Keeping this in mind, our motive is to propose various improved exponential type imputation methods and the corresponding resultant estimators by using ancillary information. The properties (biases and mean square errors) of developed estimators have been examined. It has been shown that the estimators of population mean under similar circumstances due to Prasad [1-3] and some other estimators are special case of our suggested class of estimators. Results are obtained by using simulation studies and it shows the desired performance over others. (C) 2021 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University.