Joint distribution properties of fully conditional specification under the normal linear model with normal inverse-gamma priors

Fully conditional specification (FCS) is a convenient and flexible multiple imputation approach. It specifies a sequence of simple regression models instead of a potential complex joint density for missing variables. However, FCS may not converge to a stationary distribution. Many authors have studied the convergence properties of FCS when...

How to relate potential outcomes: Estimating individual treatment effects under a given specified partial correlation

In most medical research, the average treatment effect is used to evaluate a treatment's performance. However, precision medicine requires knowledge of individual treatment effects: What is the difference between a unit's measurement under treatment and control conditions? In most treatment effect studies, such answers are not possible because...

Generalizing Univariate Predictive Mean Matching to Impute Multiple Variables Simultaneously

A blended distance to define "people-like-me"

Curve matching is a prediction technique that relies on predictive mean matching, which matches donors that are most similar to a target based on the predictive distance. Even though this approach leads to high prediction accuracy, the predictive distance may make matches look unconvincing, as the profiles of the matched donors can substantially...

Graphical and numerical diagnostic tools to assess multiple imputation models by posterior predictive checking

Missing data are often dealt with multiple imputation. A crucial part of the multiple imputation process is selecting sensible models to generate plausible values for incomplete data. A method based on posterior predictive checking is proposed to diagnose imputation models based on posterior predictive checking. To assess the congeniality of...

Partioned predictive mean matching as a large data multilevel imputation technique

Large scale assessment data often has a multilevel structure. When dealing with missing values, such structures need to be taken into account to prevent underestimation of the intraclass correlation. We evaluate predictive mean matching (PMM) as a multilevel imputation technique and compare it to other imputation approaches for multilevel data....

Predictive mean matching imputation of semicontinuous variables

Multiple imputation methods properly account for the uncertainty of missing data. One of those methods for creating multiple imputations is predictive mean matching (PMM), a general purpose method. Little is known about the performance of PMM in imputing non-normal semicontinuous data (skewed data with a point mass at a certain value and...

Pooling multiple imputations when the sample happens to be the population

Current pooling rules for multiply imputed data assume infinite populations. In some situations this assumption is not feasible as every unit in the population has been observed, potentially leading to over-covered population estimates. We simplify the existing pooling rules for situations where the sampling variance is not of interest. We...

Multiple Imputation of Squared Terms

We propose a new multiple imputation technique for imputing squares. Current methods yield either unbiased regression estimates or preserve data relations. No method, however, seems to deliver both, which limits researchers in the implementation of regression analysis in the presence of missing data. Besides, current methods only work under a...