Synthetic imputation methods for domain mean under simple random sampling

Abstract

The estimation of domain mean in presence of missing data is a significant issue in sample surveys. Literature contains no imputation method to estimate the domain mean in the presence of missing data using bivariate auxiliary information. The present study uses simple random sampling to propose several novel synthetic domain mean imputation methods and the corresponding resulting estimators based on bivariate auxiliary information in the case of missing data. To evaluate how well the suggested novel imputation methods and the resulting estimators perform, the mathematical equation of mean square errors is derived. A thorough simulation experiment is also carried out using a population drawn artificially from a normal distribution. Furthermore, an application is also offered based on real data.