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Abstract:
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For many designs, there is a nonzero probability of selecting a sample that provides poor estimates for known quantities. Stratified random sampling reduces the set of such possible samples by fixing the sample size within each stratum. However, undesirable samples are still possible with stratification. Rejective sampling removes unwanted samples by only retaining a sample if specified functions of sample estimates are within a tolerance of known values. The resulting samples are often said to be balanced on the function of the variables used in the rejection procedure. Cube sampling, an alternative to rejective sampling, attempts to select a balanced sample with the same first-order inclusion probabilities as the original design. Through simulation, we compare estimation properties of a rejective sampling procedure to those of cube sampling for estimating a mean.
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