A review on design inspired subsampling for big data

Author:

Yu Jun,Ai Mingyao,Ye Zhiqiang

Publisher

Springer Science and Business Media LLC

Subject

Statistics, Probability and Uncertainty,Statistics and Probability

Reference135 articles.

1. Ai M, Wang F, Yu J, Zhang H (2021a) Optimal subsampling for large-scale quantile regression. J Complex 62:101512

2. Ai M, Yu J, Zhang H, Wang H (2021b) Optimal subsampling algorithms for big data regressions. Stat Sin 31:749–772

3. Altschuler J, Bach F, Rudi A, Niles-Weed J (2019) Massively scalable sinkhorn distances via the nyström method. Adv Neural Inf Process Syst 32:4427–4437

4. Atkinson A, Donev A, Tobias R (2007) Optimum experimental designs, with SAS. Oxford University Press, Oxford

5. Avron H, Maymounkov P, Toledo S (2010) Blendenpik: supercharging Lapack’s least-squares solver. SIAM J Sci Comput 32:1217–1236

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