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- W2922163380 abstract "The goal of this contribution is to develop subsampling methods in the framework of big data and to show their feasibility in a simulation study. We argue that using different subsampling distributions with different subsampling sizes brings a lot of information on the behavior of statistical procedures: subsampling allows to estimate the rate of convergence of different procedures and to construct confidence intervals for general parameters including the generalization error of an algorithm in machine learning." @default.
- W2922163380 created "2019-03-22" @default.
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- W2922163380 date "2018-01-01" @default.
- W2922163380 modified "2023-10-18" @default.
- W2922163380 title "Subsampling for Big Data: Some Recent Advances" @default.
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- W2922163380 doi "https://doi.org/10.1007/978-3-319-96941-1_13" @default.
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