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Subdata selection algorithm for linear model discrimination

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  • Jun Yu

    (Beijing Institute of Technology)

  • HaiYing Wang

    (University of Connecticut)

Abstract

A statistical method is likely to be sub-optimal if the assumed model does not reflect the structure of the data at hand. For this reason, it is important to perform model selection before statistical analysis. However, selecting an appropriate model from a large candidate pool is usually computationally infeasible when faced with a massive data set, and little work has been done to study data selection for model selection. In this work, we propose a subdata selection method based on leverage scores which enables us to conduct the selection task on a small subdata set. Compared with existing subsampling methods, our method not only improves the probability of selecting the best model but also enhances the estimation efficiency. We justify this both theoretically and numerically. Several examples are presented to illustrate the proposed method.

Suggested Citation

  • Jun Yu & HaiYing Wang, 2022. "Subdata selection algorithm for linear model discrimination," Statistical Papers, Springer, vol. 63(6), pages 1883-1906, December.
    • Handle: RePEc:spr:stpapr:v:63:y:2022:i:6:d:10.1007_s00362-022-01299-8
    DOI: 10.1007/s00362-022-01299-8
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    References listed on IDEAS

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    Cited by:

    1. Amalan Mahendran & Helen Thompson & James M. McGree, 2023. "A model robust subsampling approach for Generalised Linear Models in big data settings," Statistical Papers, Springer, vol. 64(4), pages 1137-1157, August.
    2. Jun Yu & Jiaqi Liu & HaiYing Wang, 2023. "Information-based optimal subdata selection for non-linear models," Statistical Papers, Springer, vol. 64(4), pages 1069-1093, August.

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