Simplified maximum likelihood inference based on the likelihood decomposition for missing data

Summary: In this paper, we propose an estimation method when sample data are incomplete. We decompose the likelihood according to missing patterns and combine the estimators based on each likelihood weighting by the Fisher information ratio. This approach provides a simple way of estimating parameters, especially for non-monotone missing data. Numerical examples are presented to illustrate this method.