Information-based optimal subdata selection for big data logistic regression

摘要

Technological advances have enabled an exponential growth in data volumes, and proven statistical methods are no longer applicable for extraordinary large data sets due to computational limitations. Subdata selection is an effective strategy to address this issue. In this study, we investigate existing sampling approaches and propose a novel framework of selecting subsets of data for logistic regression models. We show that, while the information contained in the subdata based on random sampling approaches is limited by the size of the subset, the information contained in the subdata based on the new framework increases as the size of the full data set increases. Performances of the proposed approach and those of other existing methods are compared under various criteria via extensive simulation studies. (C) 2020 Elsevier B.V. All rights reserved.