Improving the performance of Bayesian networks in nonignorable missing data imputation

The issue of missing data may arise for researchers who deal with data gathering problems. Bayesian networks are one of the proposed methods that have been recently used in missing data imputation. The main objective of this research is to improve the efficiency of the Bayesian networks in nonignorable missing imputation, by adding missing indicator nodes for incomplete variables and constructing an augmented Bayesian network. Also, to consider the effect of different kinds of missingness mechanism (ignorable and nonignorable) on the performance of imputation methods. Four methods of imputation: random overall hot-deck imputation, within-class random hot-deck imputation, imputation using Bayesian networks and imputation using presented augmented Bayesian networks are compared using two indices: (1) a distance function and (2)Minimum Kullback-Leibler index. Results indicate the high-quality of the methods based on Bayesian networks relative to other imputation methods.