  The emergence of massive data in recent years brings challenges to automatic
statistical inference. This is particularly true if the data are too numerous
to be read into memory as a whole. Accordingly, new sampling techniques are
needed to sample data from a hard drive. In this paper, we propose a sequential
addressing subsampling (SAS) method, that can sample data directly from the
hard drive. The newly proposed SAS method is time saving in terms of addressing
cost compared to that of the random addressing subsampling (RAS) method.
Estimators (e.g., the sample mean) based on the SAS subsamples are constructed,
and their properties are studied. We conduct a series of simulation studies to
verify the finite sample performance of the proposed SAS estimators. The time
cost is also compared between the SAS and RAS methods. An analysis of the
airline data is presented for illustration purpose.
