Distributed subsampling for multiplicative regression
Author:
Funder
National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s11222-024-10477-7.pdf
Reference51 articles.
1. Ai, M., Wang, F., Yu, J., Zhang, H.: Optimal subsampling for large-scale quantile regression. J. Complex. 62, 101512 (2021). https://doi.org/10.1016/j.jco.2020.101512
2. Ai, M., Yu, J., Zhang, H., Wang, H.: Optimal subsampling algorithms for big data regressions. Stat. Sin. 31(2), 749–772 (2021). https://doi.org/10.5705/ss.202018.0439
3. Atkinson, A., Done, A., Tobias, R.: Optimum Experimental Designs, with SAS. Oxford University Press, Oxford (2007)
4. Boyd, S., Parikh, N., Chu, E., Peleato, B., Eckstein, J.: Distributed optimization and statistical learning via the alternating direction method of multipliers. Found. Trends Mach. Learn. 3(1), 1–122 (2010). https://doi.org/10.1561/2200000016
5. Boyd, S., Vandenberghe, L.: Convex Optimization. Cambridge University Press, Cambridge (2004)
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