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We consider the problem of deciding on sampling strategy, in particularsampling design. We propose a risk measure, whose minimizing value guides thechoice. The method makes use of a superpopulation model and takes into accountuncertainty about its parameters. The method is illustrated with a realdataset, yielding satisfactory results. As a baseline, we use the strategy thatcouples probability proportional-to-size sampling with the differenceestimator, as it is known to be optimal when the superpopulation model is fullyknown. We show that, even under moderate misspecifications of the model, thisstrategy is not robust and can be outperformed by some alternatives

A method to find an efficient and robust sampling strategy under model uncertainty