Real-Time Regression Analysis of Streaming Clustered Data With Possible Abnormal Data Batches

This article develops an incremental learning algorithm based on quadratic inference function (QIF) to analyze streaming datasets with correlated outcomes such as longitudinal data and clustered data. We propose a renewable QIF (RenewQIF) method within paradigm of estimation inference, in which parameter estimates are recursively renewed current summary statistics historical data, but no use any subject-level raw compare our both offline generalized estimating equations (GEE) approach that process the entire cumulative all together, show theoretically numerically procedure enjoys statistical computational efficiency. also diagnose homogeneity assumption regression coefficients via sequential goodness-of-fit test screening occurrences abnormal batches. implement proposed methodology by expanding existing Spark’s Lambda architecture for operation quality diagnosis. illustrate extensive simulation studies analysis car crash from National Automotive Sampling System-Crashworthiness Data System (NASS CDS). Supplementary materials this available online.