Information-based optimal subdata selection for non-linear models

Jun Yu (), Jiaqi Liu () and HaiYing Wang ()
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Jun Yu: Beijing Institute of Technology
Jiaqi Liu: University of Connecticut
HaiYing Wang: University of Connecticut

Statistical Papers, 2023, vol. 64, issue 4, No 5, 1069-1093

Abstract: Abstract Subdata selection methods provide flexible tradeoffs between computational complexity and statistical efficiency in analyzing big data. In this work, we investigate a new algorithm for selecting informative subdata from massive data for a broad class of models, including generalized linear models as special cases. A connection between the proposed method and many widely used optimal design criteria such as A-, D-, and E-optimality criteria is established to provide a comprehensive understanding of the selected subdata. Theoretical justifications are provided for the proposed method, and numerical simulations are conducted to illustrate the superior performance of the selected subdata.

Keywords: Generalized linear models; Information matrix; Massive data; Optimality criteria (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s00362-023-01430-3

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