  The demand of computational resources for the modeling process increases as
the scale of the datasets does, since traditional approaches for regression
involve inverting huge data matrices. The main problem relies on the large data
size, and so a standard approach is subsampling that aims at obtaining the most
informative portion of the big data. In the current paper, we explore an
existing approach based on leverage scores, proposed for subdata selection in
linear model discrimination. Our objective is to propose the aforementioned
approach for selecting the most informative data points to estimate unknown
parameters in both the first-order linear model and a model with interactions.
We conclude that the approach based on leverage scores improves existing
approaches, providing simulation experiments as well as a real data
application.
