Transformed Regression Type Estimators in the Presence of Missing Observations: Case Studies on COVID-19 Incidence in Chiang Mai, Thailand

Abstract

The transformation technique can be used to modify the shape of the variable to improve the performance of the population mean estimator. In the presence of missing data, before estimating the population mean using standard statistical methods, missing data has to be taken care of. In this study, we focus on new transformed regression type estimators when missing data are present in the study variable under the uniform nonresponse mechanism and assume that the population mean of the auxiliary variable is unavailable which usually occurs in practice. An auxiliary variable can assist by increasing the efficacy of estimating the population mean. The bias and mean square error are investigated up to the first order degree approximation using the Taylor series. A simulation and case studies on COVID-19 incidence in Chiang Mai, Thailand are used to assess the performance of the new transformed estimators. The estimated number of COVID-19 patients who have pneumonia and require high-flow oxygen and the estimated daily confirmed cases of COVID-19 in Chiang Mai from the best proposed estimator are around 17 cases and 118 cases, respectively.