Bayesian nonparametric multiple imputation of partially observed data with ignorable nonresponse

We present a new, nonparametric Bayesian method for multiple imputation of partially observed data for which the pattern of missingness is arbitrary and the data are missing at random with ignorable nonresponse with respect to the model specification. Motivation for the method is provided, followed by an overview of Polya trees and their application to multiple imputation, and a comparison of the new method to existing approaches is presented.The method is illustrated on a dataset of colleges and universities in the United States.