Online Statistical Inference

Online statistical inference focuses on performing statistical analysis on data streams, updating models and inferences in real-time as new data arrives, unlike traditional methods that analyze static datasets. Current research emphasizes developing efficient algorithms, such as adaptive stochastic gradient descent and Bayesian neural networks, for online parameter estimation and hypothesis testing within various frameworks, including generalized linear models, contextual bandits, and matrix completion. This field is crucial for handling the massive, continuously generated data in modern applications, enabling timely decision-making and improved model accuracy in domains ranging from finance to personalized recommendations. The development of robust and computationally efficient online inference methods is driving significant advancements across numerous scientific disciplines.