Handling Missing Data: Expectation-Maximization Algorithm and Markov Chain Monte Carlo Algorithm
Mu Shou · 2011
Dataset with missing data is quite common in psychological research, which usually creates major problems in statistical inference. Maximum Likelihood Estimator and Multiple Imputation based on Bayesian Estimator are most important methods of handling missing data. Expectation-Maximization Algorithm, included in Maximum Likelihood Estimator is quite advantageous to flexible use and accurate results, while Markov Chain Monte Carlo Algorithm may achieve multiple imputation more easily and can be applied to handling missing data in complex situations. Finally, suitable statistical software is discussed in the field of psychological study.