Multiple Imputation

Multiple Imputation by Chained Equations (MICE) allows most models to be fit to a dataset with missing values on the independent and/or dependent variables, and provides rigorous standard errors for the fitted parameters. The basic idea is to treat each variable with missing values as the dependent variable in a regression, with some or all of the remaining variables as its predictors

Joint Multiple Imputation (JOMO) commonly assumes that the incomplete variables follow a multivariate normal distribution, often referred to as multivariate normal imputation and, under this assumption, provides rigorous standard errors for the fitted parameters