Lan Luo - Colloquium Speaker

Faculty Candidate for Assistant Professor in Data Science; Doctoral Candidate in Biostatistics, University of Michigan
Date: 
Tuesday, February 18, 2020 - 3:30pm
Colloquium Title: 
Renewable Estimation and Incremental Inference in Streaming Data Analysis
Location: 
Reception at 3:00 p.m. in 241 SH / Talk at 3:30 in 71 SH

Abstract:

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. Lan Luo will discuss both conceptual understanding and theoretical guarantees of the method and illustrate its performance via numerical examples. This is joint work with her supervisor Professor Peter Song.