Optimal subsampling from Big Datasets in presence of misspecification

In the era of Big Data, analyzing entire datasets is often infeasible due to high computational costs and the risks of overfitting. Subsampling techniques have emerged as a cost-effective alternative, enabling researchers to extract informative subsets of data. This study investigates the performance of design-based subsampling methods under conditions of model misspecification and the presence of outliers. A comparative analysis of model-based approaches, such as D-optimal and LowCon, and model-free techniques, including MRSS, Twinning, and PDDS, is conducted. Simulation studies offer insights into the trade-offs between inferential robustness and computational efficiency, guiding the selection of optimal subsampling strategies for various applications.