Selected Work
Selected Work
Missing data is a common obstacle faced by researchers. Various techniques have been available to deal with missing data problem, among which multiple imputation is commonly used. Auxiliary information could help with generating completed data sets following procedures proposed in MD-AM framework (Akande et al, 2019). This work extends the MD-AM framework to incorporate survey weights. We aim to generate completed data sets that produce survey weighted estimates in accordance with auxiliary information available from other sources. We present simulation studies of combinations of various missing data mechanisms to show the usage of three algorithms. We illustrate the methodology with an application examining voter turnout in the Current Population Survey (CPS).
Often data analysts use probabilistic record linkage techniques to match records across two data sets. Such matching can be the primary goal, or it can be a necessary step to analyze relationships among the variables in the data sets. We propose a Bayesian hierarchical model that allows data analysts to do linear regression and probabilistic record linkage simultaneously. This allows analysts to leverage relationships among the variables to improve linkage quality. Further, it enables analysts to propagate uncertainty in a principled way, while also potentially offering more accurate estimates of regression parameters compared to approaches that use a two-step process, i.e., link the records first, then estimate the linear regression on the linked data. We propose and evaluate three Markov chain Monte Carlo algorithms for implementing the Bayesian model, which we compare against a two-step process.