  If the assumed model does not accurately capture the underlying structure of
the data, a statistical method is likely to yield sub-optimal results, and so
model selection is crucial in order to conduct any statistical analysis.
However, in case of massive datasets, the selection of an appropriate model
from a large pool of candidates becomes computationally challenging, and
limited research has been conducted on data selection for model selection. In
this study, we conduct subdata selection based on the A-optimality criterion,
allowing to perform model selection on a smaller subset of the data. We
evaluate our approach based on the probability of selecting the best model and
on the estimation efficiency through simulation experiments and two real data
applications.
