We consider the relative performance of two common approaches to multiple imputation
(MI): joint MI, in which the data are modeled as a sample from a joint distribution; and …

Multiple imputation is a common approach for dealing with missing values in statistical
databases. The imputer fills in missing values with draws from predictive models estimated …

Our mi package in R has several features that allow the user to get inside the imputation
process and evaluate the reasonableness of the resulting models and imputations. These …

Conventional multiple imputation (MI)(Rubin, 1987) replaces the missing values in a dataset
by m> 1 sets of simulated values. We describe a two-stage extension of MI in which the …

With the advent of general purpose packages that support multiple imputation for analyzing
datasets with missing data (eg, Solas, SAS PROC MI, and S-Plus 6.0), we expect much …

It is common in applied research to have large numbers of variables with mixed data types
(continuous, binary, ordinal or nomial) measures on a modest number of cases. Also, even a …

The goal of multiple imputation is to provide valid inferences for statistical estimates from
incomplete data. To achieve that goal, imputed values should preserve the structure in the …

The most widely used method of multiple imputation is the MCMC algorithm based on the
multivariate normal model. While this method is often used to impute binary and polytomous …

Multiple imputation is a technique for handling data sets with missing values. The method
fills in the missing values several times, creating several completed data sets for analysis …

Empirical data are seldom completely observed. How to adequately analyse data sets
affected by missing values is usually not the focus of courses at bachelor or master level …