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Modern Subsampling Methods for Large-Scale Least Squares Regression

Modern Subsampling Methods for Large-Scale Least Squares Regression
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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

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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.

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