The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Modern Subsampling Methods for Large-Scale Least Squares Regression
|
|
Author(s): Tao Li (Institute of Statistics and Big Data, Renmin University of China, China)and Cheng Meng (Institute of Statistics and Big Data, Renmin University of China, China)
Copyright: 2020
Volume: 2
Issue: 2
Pages: 28
Source title:
International Journal of Cyber-Physical Systems (IJCPS)
Editor(s)-in-Chief: Amjad Gawanmeh (University of Dubai, United Arab Emirates)
DOI: 10.4018/IJCPS.2020070101
Purchase
|
Abstract
Subsampling methods aim to select a subsample as a surrogate for the observed sample. As a powerful technique for large-scale data analysis, various subsampling methods are developed for more effective coefficient estimation and model prediction. This review presents some cutting-edge subsampling methods based on the large-scale least squares estimation. Two major families of subsampling methods are introduced: the randomized subsampling approach and the optimal subsampling approach. The former aims to develop a more effective data-dependent sampling probability while the latter aims to select a deterministic subsample in accordance with certain optimality criteria. Real data examples are provided to compare these methods empirically, respecting both the estimation accuracy and the computing time.
Related Content
|
Hafiz Muhammad Umair Munir, Waqar Shahid Qureshi.
© 2022.
17 pages.
|
|
Antoine Trad.
© 2021.
24 pages.
|
|
Patricia G. Foley, Kathleen Hargiss, Caroline Howard, Anne Pesanvento.
© 2021.
12 pages.
|
|
Maria Lai-Ling Lam, Kei-Wing Wong.
© 2021.
20 pages.
|
|
Alexander Shamliev, Peter Mitrouchev, Maya Dimitrova.
© 2020.
19 pages.
|
|
Marina Santini, Min-Chun Shih.
© 2020.
13 pages.
|
|
Zhijing Ye, Fei Hu, Lin Zhang, Zhe Chu, Zheng O'Neill.
© 2020.
23 pages.
|
|
|