Optimum Stratified Sampling Using Prior Information

Abstract The question of how the well-known Neyman stratified allocation result generalizes when it is formally assumed that there is prior information concerning the unknown stratum means is dealt with here. This prior information is taken to be expressible in the form of a multivariate normal prior distribution. Several methods of assessing prior distributions are discussed. The allocation for stratified sampling is shown to be a special case of a more general allocation problem. A computational algorithm is presented for this more general problem of finding the allocation of sampling effort which minimizes the posterior variance of any given linear combination of unknown normal process means subject to a budget constraint. A feature of the solution is that for limited budgets one may rely solely on his prior information concerning some strata, sampling only in a subset of the strata. Finally, several applications are briefly described including a “non-Bayesian” solution to a particular problem of allocation for a multipurpose stratified sample.