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

TL;DR

This paper introduces a sequential addressing subsampling (SAS) method for efficient data sampling directly from hard drives, improving speed over traditional random addressing methods for massive data analysis under memory constraints.

Contribution

The paper proposes the SAS method, a novel subsampling technique that reduces addressing time and enables effective statistical inference from large datasets stored on disk.

Findings

SAS method is faster than RAS in addressing cost.

SAS estimators have desirable statistical properties.

Application to airline data demonstrates practical utility.

Abstract

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.