Fast Generalized Linear Models by Database Sampling and One-Step Polishing

In this article, I show how to fit a generalized linear model to <i>N</i> observations on <i>p</i> variables stored in a relational database, using one sampling query and one aggregation query, as long as N12+δ observations can be stored in memory, for some δ&gt;0. The resulting estimator is fully efficient and asymptotically equivalent to the maximum likelihood estimator, and so its variance can be estimated from the Fisher information in the usual way. A proof-of-concept implementation uses R with MonetDB and with SQLite, and could easily be adapted to other popular databases. I illustrate the approach with examples of taxi-trip data in New York City and factors related to car color in New Zealand. Supplementary materials for this article are available online.