A Multiple Imputation for Reducing Outlier Effect

Multiple Imputation for Missing Data

Multiple imputation provides a useful strategy for dealing with data sets with missing values. Instead of filling in a single value for each missing value, Rubin’s (1987) multiple imputation procedure replaces each missing value with a set of plausible values that represent the uncertainty about the right value to impute. These multiply imputed data sets are then analyzed by using standard proc...

Multiple Imputation for Causal Inference

The potential outcome framework for causal inference is fundamentally a missing data problem with a special, the so-called file-matching, pattern of missing data. Given the large body of literature on various methods for handling missing data and associated software, it will be useful to use such methods to facilitate causal inference for routine applications. This article uses the sequential r...

Multiple Imputation with Mplus

Model - Based Multiple Imputation

managing Multiple imputation can improve data quality.

Local Multiple Imputation

Dealing with missing data via parametric multiple imputation methods usually implies stating several strong assumptions about both the distribution of the data and about underlying regression relationships. If such parametric assumptions do not hold, the multiply imputed data are not appropriate and might produce inconsistent estimators and thus misleading results. In this paper, a fully nonpar...