Post-Stratification against Bias in Sampling

Summary For a quite general sampling model, allowing bias (due to undercoverage, nonresponse, for example), the post-stratified estimator of the population mean is shown to be maximum likelihood and have a minimal variance property. Bounds are calculated for bias and variance. In an illustration it is shown how these bounds may be used to obtain approximate confidence intervals.