A Combined Transformed Variable for Population Mean Estimators When Missing Data Occur with an Application to COVID-19 Incidence

Abstract

COVID-19 has killed many people and continues to be a major problem in all countries around the world. Estimating COVID-19 data in advance is helpful for the World Health Organization and governments in countries all over the globe to prepare the necessary resources. However, some of this information may be missing and needs to be dealt with before processing to estimation. The transformation method of an auxiliary variable can assist by increasing the performance of estimating the population mean. A combined transformed variable is suggested for estimating population mean when a study variable contains some missing values with uniform nonresponse, and it is applied in an application to data on COVID-19 incidence. The bias and mean square error of the suggested estimator are investigated and the performance is compared with existing estimators via a simulation study and an application to COVID-19 data. The results show that the suggested combined transformed estimators overtake existing estimators in terms of higher efficiency which yields the estimated value of total deaths of COVID-19 equal to 29497 cases.