A spatially-adjusted Bayesian additive regression tree model to merge two datasets

Abstract

Scientific hypotheses of interest often involve variables that are not available in a single survey. This is a common problem for researchers working with survey data. We propose a model-based approach to provide information about the missing variable. We use a spatial extension of the BART (Bayesian additive regression tree) model. The imputation of the missing variables and inference about the relationship between two variables are obtained simultaneously as posterior inference under the proposed model. The uncertainty due to imputation is automatically accounted for. A simulation analysis and an application to data on self-perceived health status and income are presented.