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

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journal contribution
posted on 2022-01-20, 16:20 authored by Lan Luo, Ling Zhou, Peter X.-K. Song

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 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 both offline QIF and offline generalized estimating equations (GEE) approach that process the entire cumulative subject-level data all together, and show theoretically and numerically that our renewable procedure enjoys statistical and computational efficiency. We also propose an approach to diagnose the homogeneity assumption of regression coefficients via a sequential goodness-of-fit test as a screening procedure on occurrences of abnormal data batches. We implement the proposed methodology by expanding existing Spark’s Lambda architecture for the operation of statistical inference and data quality diagnosis. We illustrate the proposed methodology by extensive simulation studies and an analysis of streaming car crash datasets from the National Automotive Sampling System-Crashworthiness Data System (NASS CDS). Supplementary materials for this article are available online.


This research was partially supported by the National Science Foundation grants DMS1811734 and DMS2113564 to Song, and the National Natural Science (Nos. 11901470, 11931014 and 11829101) and the Fundamental Research Funds for the Central Universities (Nos. JBK190904 and JBK1806002) to Zhou.