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Abstract:
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Ranked-set sampling is an approach that leads to improved statistical inference in situations where the units to be sampled can be ranked relative to each other before formal measurement. This ranking need not be completely accurate. In fact, results in the literature have shown that no matter how poor the ranking, procedures based on ranked-set sampling tend to be at least as efficient as procedures based on simple random sampling. However, efforts to quantify the gains in efficiency for ranked-set sampling procedures have been hampered by a shortage of models for imperfect rankings. In this paper, we introduce a new class of models for imperfect rankings and prove that essentially any reasonable model for imperfect rankings is a limit of models in this class. We then describe a specific, easily applied method for selecting an appropriate model from the class.
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