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In this note, I show how to fit a generalised linear model to $N$observations on $p$ variables stored in a relational database, using onesampling query and one aggregation queries, as long as $N^{\frac{1}{2}+\delta}$observations can be stored in memory. The resulting estimator is fullyefficient and asymptotically equivalent to the maximum likelihood estimator,and so its variance can be estimated from the Fisher information in the usualway. A proof-of-concept implementation uses R with MonetDB and with SQLite, andcould easily be adapted to other popular databases. I illustrate the approachwith examples of taxi-trip data in New York City and factors related to carcolour in New Zealand.

Fast generalised linear models by database sampling and one-step polishing