Online Inference in High-Dimensional Models

Published in to submit, 2023

Online statistical inference refers to an inferential method where, as data sequentially becomes available, it’s employed to update the model’s parameters at every step. This approach has broad applications in fields such as network security, quantitative finance, and recommendation systems, among others. In contrast to offline techniques where the entire dataset is used for training, online learning algorithms are anticipated to offer computational efficiency while still delivering statistical results that are comparable to their offline counterparts.

Recommended citation: Han, R., Luo, L., Luo, Y., Lin, Y., & Huang, J. (2023). Online Inference in High-dimensional Models. To submit.