  Subsampling methods aim to select a subsample as a surrogate for the observed
sample. As a powerful technique for large-scale data analysis, various
subsampling methods are developed for more effective coefficient estimation and
model prediction. This review presents some cutting-edge subsampling methods
based on the large-scale least squares estimation. Two major families of
subsampling methods are introduced, respectively, the randomized subsampling
approach and the optimal subsampling approach. The former aims to develop a
more effective data-dependent sampling probability, while the latter aims to
select a deterministic subsample in accordance with certain optimality
criteria. Real data examples are provided to compare these methods empirically,
respecting both the estimation accuracy and the computing time.
