Spatially Correlated Missing Data: A Comparative Study

Abstract

Missing data is an important and well-researched topic in the literature, but there is a lack of analysis on the missing data problem in the context of spatially correlated data. Multiple imputation methods in the literature are not tailored for spatial data. This simulation study aimed to determine the potential gain of spatial imputation models over existing multiple imputation approaches. The bias, confidence interval width and coverage were used to compare the performance of imputation models on simulated data. The spatial lag model for imputation was applied to the Boston dataset to assess performance on real data. The spatial models were found to have potential improvement over existing methods, but are potentially sensitive to the missing not at random mechanism.