Orthogonal subsampling for big data linear regression

Lin Wang, Jake Elmstedt, Weng Kee Wong, Hongquan Xu

研究成果: Article同行評審

19 引文 斯高帕斯(Scopus)


The dramatic growth of big datasets presents a new challenge to data storage and analysis. Data reduction, or subsampling, that extracts useful information from datasets is a crucial step in big-data analysis. We propose an orthogonal subsampling (OSS) approach for big data with a focus on linear regression models. The approach is inspired by the fact that an orthogonal array of two levels provides the best experimental design for linear regression models in the sense that it minimizes the average variance of the estimated parameters and provides the best predictions. The merits of OSS are three-fold: (i) it is easy to implement and fast; (ii) it is suitable for distributed parallel computing and ensures the subsamples selected in different batches have no common data points, and (iii) it outperforms existing methods in minimizing the mean squared errors of the estimated parameters and maximizing the efficiencies of the selected subsamples. Theoretical results and extensive numerical results show that the OSS approach is superior to existing subsampling approaches. It is also more robust to the presence of interactions among covariates, and, when they do exist, OSS provides more precise estimates of the interaction effects than existing methods. The advantages of OSS are also illustrated through analysis of real data.

頁(從 - 到)1273-1290
期刊Annals of Applied Statistics
出版狀態Published - 2021 9月

All Science Journal Classification (ASJC) codes

  • 統計與概率
  • 建模與模擬
  • 統計、概率和不確定性


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