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An Econometric Perspective on Algorithmic Subsampling
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DOI：10.1146/annurev-economics-022720-114138.png)
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摘要
En 中文
Data sets that are terabytes in size are increasingly common, but computer bottlenecks often frustrate a complete analysis of the data, and diminishing returns suggest that we may not need terabytes of data to estimate a parameter or test a hypothesis. But which rows of data should we analyze, and might an arbitrary subset preserve the features of the original data? We review a line of work grounded in theoretical computer science and numerical linear algebra that finds that an algorithmically desirable sketch, which is a randomly chosen subset of the data, must preserve the eigenstructure of the data, a property known as subspace embedding. Building on this work, we study how prediction and inference can be affected by data sketching within a linear regression setup. We use statistical arguments to provide inferenceconscious guides to the sketch size and show that an estimator that pools over different sketches can be nearly as efficient as the infeasible one using the full sample.
Keyword:
sketching
coresets
subspace embedding
countsketch
uniform sampling
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期刊
IF:
11.4
论文数:
356
被引数:
4.5K
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