Online Program Home
  My Program

All Times EDT

Abstract Details

Activity Number: 203 - Contemporary Machine Learning
Type: Contributed
Date/Time: Tuesday, August 4, 2020 : 10:00 AM to 2:00 PM
Sponsor: Section on Statistical Learning and Data Science
Abstract #313029
Title: Real-Time Regression Analysis of Streaming Clustered Data Sets
Author(s): Lan Luo* and Peter X.K. Song
Companies: University of Michigan and University of Michigan
Keywords: Online learning; Incremental statistical analysis; Quadratic inference function; Generalized estimating equation; Lambda architecture; Correlated data analysis

This paper develops an incremental learning algorithm based on quadratic inference function (QIF) to analyze streaming datasets with correlated outcomes such as longitudinal and clustered data. We propose a renewable QIF (RenewQIF) method within a paradigm of renewable estimation and incremental inference, in which parameter estimates are recursively renewed with current data and summary statistics of historical data, but with no use of any historical subject-level raw data. We compare our renewable estimation method with the oracle generalized estimating equations (GEE) approach that processes the entire cumulative subject-level data, and show theoretically and numerically that our renewable procedure enjoys statistical and computational efficiency. We implement the proposed methodology by expanding existing Spark's Lambda architecture to accommodate the screening tool box, which is examined via extensive simulation studies. Also, we illustrate the proposed method by an analysis of streaming car crash datasets from the National Automotive Sampling System-Crashworthiness Data System (NASS CDS).

Authors who are presenting talks have a * after their name.

Back to the full JSM 2020 program