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The emergence of massive data in recent years brings challenges to automaticstatistical inference. This is particularly true if the data are too numerousto be read into memory as a whole. Accordingly, new sampling techniques areneeded to sample data from a hard drive. In this paper, we propose a sequentialaddressing subsampling (SAS) method, that can sample data directly from thehard drive. The newly proposed SAS method is time saving in terms of addressingcost 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 toverify the finite sample performance of the proposed SAS estimators. The timecost is also compared between the SAS and RAS methods. An analysis of theairline data is presented for illustration purpose.

A Sequential Addressing Subsampling Method for Massive Data Analysis under Memory Constraint