Comparison between EM Algorithm and Multiple Imputation on Predicting Children’s Weight at School Entry

Abstract

EM Algorithm and Multiple Imputation are widely used methods in dealing with missing data. Although Multiple Imputation always be the favourite choice of researcher due to its accuracy and simple application, but the issue arises whether EM algorithm perform better with several times of imputation. Both methods will be tested using different number of imputations with the help of Amelia and Mice package in R software. The imputed data sets are compared using model averaging with Corrected Akaike Information Criteria (AICC) as model selection Criterion. External validation and mean squared error of prediction (MSE(P)) are used to determine the best imputation method. Gateshead Millennium Study (GMS) data on children weight will illustrate the comparison between EM Algorithm and Multiple imputation. The results show that Multiple imputation performs slightly better compared to EM Algorithm.