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Optimal representative sample weighting

机译:最佳代表性样品加权

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摘要

We consider the problem of assigning weights to a set of samples or data records, with the goal of achieving a representative weighting, which happens when certain sample averages of the data are close to prescribed values. We frame the problem of finding representative sample weights as an optimization problem, which in many cases is convex and can be efficiently solved. Our formulation includes as a special case the selection of a fixed number of the samples, with equal weights, i.e., the problem of selecting a smaller representative subset of the samples. While this problem is combinatorial and not convex, heuristic methods based on convex optimization seem to perform very well. We describe our open-source implementation rsw and apply it to a skewed sample of the CDC BRFSS dataset.
机译:我们考虑将权重到一组样本或数据记录分配的问题,其目的是实现代表加权,这发生在数据的某些样本平均值接近规定值时发生。我们框架将代表性样本权重框架作为优化问题的问题,这在许多情况下是凸的并且可以有效地解决。我们的配方包括选择固定数量的样本的特殊情况,其中重量相等,即选择样本较小的代表性子集的问题。虽然这个问题是组合而不是凸,基于凸优化的启发式方法似乎表现得很好。我们介绍了我们的开源实现范权,并将其应用于CDC BRFSS数据集的偏斜样本。

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