A Non-Iterative Bayesian Approach to Statistical Matching

Data fusion or statistical matching techniques merge datasets from different survey samples to achieve a complete but artificial data file which contains all variables of interest. The merging of datasets is usually done on the basis of variables common to all files, but traditional methods implicitly assume conditional independence between the variables never jointly observed given the common variables. Therefore we suggest using model based approaches tackling the data fusion task by more flexible procedures. By means of suitable multiple imputation techniques, the identification problem which is inherent in statistical matching is reflected. Here a non-iterative Bayesian version of Rubin's implicit regression model is presented and compared in a simulation study with imputations from a data augmentation algorithm as well as an iterative approach using chained equations.