Renewable Estimation and Incremental Inference in Streaming Data Analysis
New data collection and storage technologies have given rise to a new field of streaming data analytics, including real-time statistical methodology for online data analyses. Streaming data refers to high-throughput recordings with large volumes of observations gathered sequentially and perpetually over time. Such type of data includes national disease registry, mobile health, and disease surveillance, among others. This talk primarily concerns the development of a fast real-time statistical estimation and inference method for regression analysis, with a particular objective of addressing challenges in streaming data storage and computational efficiency. Termed as renewable estimation, this method enjoys strong theoretical guarantees, including both asymptotic unbiasedness and estimation efficiency, and fast computational speed. The key technical novelty pertains to the fact that the proposed method uses current data and summary statistics of historical data. The proposed algorithm will be demonstrated in generalized linear models (GLM) for cross-sectional data. I will discuss both conceptual understanding and theoretical guarantees of the method and illustrate its performance via numerical examples. This is joint work with my supervisor Professor Peter Song.