Information-based optimal subdata selection for non-linear models

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.