论文标题
一种在模型不确定性下找到高效且可靠的采样策略的方法
A method to find an efficient and robust sampling strategy under model uncertainty
论文作者
论文摘要
我们考虑确定采样策略的问题，特别是采样设计。我们提出了一项风险度量，其最小化价值指导了选择。该方法利用了超级人群模型，并考虑了有关其参数的不确定性。该方法用真实的数据集说明，得出令人满意的结果。作为基准，我们使用的策略将概率与差异估计器的概率成比例的大小采样器相结合，因为当超级人群模型完全知道时，它是最佳的。我们表明，即使在模型中适度的错误指定下，此策略也不强大，并且可以胜过某些替代方案
We consider the problem of deciding on sampling strategy, in particular sampling design. We propose a risk measure, whose minimizing value guides the choice. The method makes use of a superpopulation model and takes into account uncertainty about its parameters. The method is illustrated with a real dataset, yielding satisfactory results. As a baseline, we use the strategy that couples probability proportional-to-size sampling with the difference estimator, as it is known to be optimal when the superpopulation model is fully known. We show that, even under moderate misspecifications of the model, this strategy is not robust and can be outperformed by some alternatives
