Date of Completion
Missing data; Multiple imputation
Dr. Ofer Harel
Dr. Dipak Dey
Dr. Jun Yan
Field of Study
Doctor of Philosophy
This research presents a framework for generating multiple imputations when we wish to incorporate the uncertainty of prior distributions utilized in the imputation phase. Imputations are generated from multiple posterior predictive distributions based on different prior distributions. Parameter estimates under each imputation model are combined using the rules of nested multiple imputation. Through the use of simulation, we investigate the impact of prior distribution uncertainty on post-imputation inferences and show that incorporating this uncertainty improves the coverage of parameter estimates. We apply our method to several research studies with various parameters of interest where prevalence of missing data was a concern. We show that different assumptions of prior distributions can have substantial impact on inference.
Pare, Valerie, "Impact of Prior Distribution Uncertainty in Multiple Imputation Inference" (2014). Doctoral Dissertations. 319.