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Multiple Imputation for Continuous and Categorical Data: Comparing Joint Multivariate Normal and Conditional Approaches
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Kropko, Jonathan; Goodrich, Ben; Gelman, Andrew; Hill, Jennifer – Grantee Submission, 2014
We consider the relative performance of two common approaches to multiple imputation (MI): joint multivariate normal (MVN) MI, in which the data are modeled as a sample from a joint MVN distribution; and conditional MI, in which each variable is modeled conditionally on all the others. In order to use the multivariate normal distribution,…
Descriptors: Statistical Analysis, Multivariate Analysis, Accuracy, Data