Development of Imputation Methods for Missing Data in Multiple Linear Regression Analysis

Missing data is a common issue in many domains of study. If this issue is disregarded, the erroneous conclusion may be reached. This study's objective is to develop and compared the efficiency of eight imputation methods: hot deck imputation (HD), k-nearest neighbors imputation (KNN), stochastic regression, imputation (SR), predictive mean matching imputation (PMM), random forest imputation (RF), stochastic regression random forest with equivalent weight imputation (SREW), k-nearest random forest with equivalent weight imputation (KREW), and k-nearest stochastic regression and random forest with equivalent weight imputation (KSREW). In this study, the simulation was run using sample sizes of 30, 60, 100, and 150, and missing percentages of 10%, 20%, 30%, and 40%. The average mean square error (AMSE) was used to compare efficiency. The results reveal that the proposed composite approaches outperformed the single ones, particularly a three-component method called KSREW. Increasing the number of components to a four-component method, on the other hand, has no effect on imputation performance.