STOR Colloquium: Lan Luo, University of Michigan
University of Michigan
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 data collection scheme is pervasive not only in biomedical sciences such as mobile health, but also in other fields such as IT, finance, service and operations etc. This talk primarily concerns the development of a 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 asymptotic consistency and statistical efficiency, as well as 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 and quadratic inference functions (QIF) for correlated 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.
Refreshments will be served at 3:00pm in the 3rd floor lounge of Hanes Hall