Bayesian predictive inference when integrating a non-probability sample and a probability sample

Abstract: We consider the problem of integrating a small probability sample (ps) and a non-probability sample (nps). By definition, for the nps, there are no survey weights, but for the ps, there are survey weights. The key issue is that the nps, although much larger than the ps, can lead to a biased estimator of a finite population quantity but with much smaller variance. We begin with a relatively simple problem in which the population is assumed to be homogeneous and there are no common units in the ps and the nps. We assume that there are covariates and responses for everyone in the two samples, and there are no covariates available for the nonsampled units. We use the nps (ps) to construct a prior for the ps (nps). We also introduce partial discounting to avoid a dominance of the prior. We use Bayesian predictive inference for the finite population mean. In our illustrative example on body mass index and our simulation study, we compare the relative performance of alternative procedures and demonstrate that our procedure leads to improved estimates over the ps only estimate.