Robust and efficient subsampling algorithms for massive data logistic regression-Reference-Cited by-同舟云学术

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Robust and efficient subsampling algorithms for massive data logistic regression

Published:2023-04-26 Issue: Volume: Page:1-19
ISSN:0266-4763
Container-title:Journal of Applied Statistics
language:en
Short-container-title:Journal of Applied Statistics

Author:

Jin Jun¹,Liu Shuangzhe²^ORCID,Ma Tiefeng³

Affiliation:

1. College of Mathematical Sciences, Yangzhou University, Yangzhou, People's Republic of China

2. Faculty of Science and Technology, University of Canberra, Canberra, Australia

3. School of Statistics, Southwestern University of Finance and Economics, Chengdu, People's Republic of China

Funder

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

Publisher

Informa UK Limited

Subject

Statistics, Probability and Uncertainty,Statistics and Probability

Link

https://www.tandfonline.com/doi/pdf/10.1080/02664763.2023.2205611

Reference30 articles.

1. OPTIMAL SUBSAMPLING ALGORITHMS FOR BIG DATA REGRESSIONS

2. K. Bhatia, P. Jain, and P. Kar, Robust Regression Via Hard Thresholding, in Advances in Neural Information Processing Systems, Curran Associates, Inc., Cambridge, MA, 2015, pp. 721–729.

3. Target estimation for the logistic regression model

4. Using balanced iterative reducing and clustering hierarchies to compute approximate rank statistics on massive datasets

5. Robust learning from bites for data mining

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