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.
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Acknowledgements
This research has been conducted as part of the project Labex MME-DII (ANR11-LBX-0023- 01), and the industrial chair “Machine Learning for Big Data,” Télécom- -ParisTech.
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Bertail, P., Jelassi, O., Tressou, J., Zetlaoui, M. (2018). Subsampling for Big Data: Some Recent Advances. In: Bertail, P., Blanke, D., Cornillon, PA., Matzner-Løber, E. (eds) Nonparametric Statistics. ISNPS 2016. Springer Proceedings in Mathematics & Statistics, vol 250. Springer, Cham. https://doi.org/10.1007/978-3-319-96941-1_13
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