  This paper 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, 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).
