A new hybrid method for data analysis when a significant percentage of data is missing

This article aims to compare the efficiency of different imputation methods with missing data. In this way we use mean, median, Expected-Maximization (EM), regression imputation(RI) and multiple imputations (MI) to replace missing data.In fact, we employ three proposed combination methods, namely EM imputation with MI imputation (EMMI), EM imputation with regression imputation (EMR), and regression imputation with MIimputation (MI). In this paper, we compare these methods using an example study of Waterborne Container Trade by the US Customs Port (2000-2017) where the methods with different missing percent-ages. Several criteria, are used to compare estimations efficiency, such as mean, Standard Deviation (SD), and Mean Squared Error (MSE). The results show that the efficiency of composite imputation methods in almost all situations, in terms of MSE, RMI imputation method outperforms other methods. Nevertheless, when the missing percentage is small, the EMR imputation method performs better. In terms of the SD criterion, we find that the MI method is better than the other methods, where the RMI method is good when the missing percentage is large. When the missing percentage is in the range (40-50%), the EMR and RMI imputation methods give a better MSE.