Some efficient estimators in case of missing data

To overcome the missing data in sample surveys, the imputation technique plays a significant role in estimating the population mean of the study variable. Clinical data, agricultural data and business data are well-suited examples for this. Keeping this in mind, we have suggested three exponential type imputation methods and the corresponding estimators by making the optimal use of an auxiliary variable. The properties of suggested estimators have been investigated under large sample approximations. The results obtained in this study have been compared with mean estimator, ratio estimator, Kadilar and Cingi (2008) estimators, Diana and Perri (2010) estimators, Al-Omari, Bouza, and Herrera (2013) estimators, and recently Bhushan and Pandey (2016) estimators, Singh et al. (2016) estimators and Singh, Pandey, and Sharma (2020) estimators. It has been shown empirically that the suggested estimators perform effectively over all other estimators. A simulation study is also evaluated in the validation of the present work.