Optimal representative sample weighting

被引:0
|
作者
Shane Barratt
Guillermo Angeris
Stephen Boyd
机构
[1] Stanford University,Electrical Engineering Department
来源
Statistics and Computing | 2021年 / 31卷
关键词
Sample weighting; Iterative proportional fitting; Convex optimization; Distributed optimization;
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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.
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