The simulation-based imputation methods for handling missing data in double sampling scheme

Missing data is a pervasive issue in the field of data science, leading to biased estimates and a reduction in statistical power. To address this challenge, double sampling schemes have emerged as a cost-effective strategy. However, selecting the most suitable imputation method for handling missing data in double sampling scheme remains a complex task. This paper proposes improved imputation methods under a double-sampling setup. It uses the simulation-based approach to evaluate and compare different imputation methods with proposed ones for effectively handling missing data in double-sampling scenarios. Consider various imputation techniques, including mean imputation and regression imputation. Each imputation method is applied to incomplete datasets, and the performance of the estimators is evaluated based on metrics such as mean squared error and percentage relative efficiency. In conclusion, our simulation-based approach comprehensively evaluates various imputation techniques in the context of double sampling. The insights gained from this study can assist researchers in making informed decisions when handling missing data, ultimately improving the validity and reliability of statistical inferences in double-sampling schemes.