Online Updating of Statistical Inference in the Big Data Setting

We present statistical methods for big data arising from online analytical processing, where large amounts of data arrive in streams and require fast analysis without storage/access to the historical data. In particular, we develop iterative estimating algorithms and statistical inferences for linear models and estimating equations that update as new data arrive. The online updating framework in the linear model setting introduces predictive residuals that can be used to test the goodness-of-fit of the hypothesized model. In simulation studies, our approach compares favorably with competing approaches in terms of timing and accuracy. Joint work with Ming-Hui Chen, Jun Yan, Chun Wang, Jing Wu (Department of Statistics, University of Connecticut)