New Transformed Estimators in the Presence of Missing Data: A Case Study on COVID-19

The COVID-19 pandemic is the most concerning issue around the world now, it plays an important role in both the public health and economy of every country. Determining information related to COVID-19 can help the world to be prepared for situations that arise from this epidemic. COVID-19 mortality data may not be recorded which results in missing data. Handling missing data should considered in the first place before the estimation process. In this study, a class of ratio estimators using a transformed auxiliary variable has been suggested in the presence of missing data in the study variable under simple random sampling. The bias and mean square error of the proposed class of estimators are investigated. Simulation studies and an application to COVID-19 data have been examined to see the performance of the proposed estimators. The results show that the proposed estimators perform the best and give at least two times higher efficiency than the existing estimators.