Sampling Techniques for Big Data Analysis

In analysing big data for finite population inference, it is critical to adjust for the selection bias in the big data. In this paper, we propose two methods of reducing the selection bias associated with the big data sample. The first method uses a version of inverse sampling by incorporating auxil...

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Bibliographic Details
Published in:International statistical review 2019-05, Vol.87 (S1), p.S177-S191
Main Authors: Kim, Jae Kwang, Wang, Zhonglei
Format: Article
Language:English
Subjects:
Bias
Big Data
Data analysis
Data integration
Data management
inverse sampling
non‐probability sample
Sampling
selection bias
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Summary:In analysing big data for finite population inference, it is critical to adjust for the selection bias in the big data. In this paper, we propose two methods of reducing the selection bias associated with the big data sample. The first method uses a version of inverse sampling by incorporating auxiliary information from external sources, and the second one borrows the idea of data integration by combining the big data sample with an independent probability sample. Two simulation studies show that the proposed methods are unbiased and have better coverage rates than their alternatives. In addition, the proposed methods are easy to implement in practice.
ISSN:0306-7734
1751-5823
DOI:10.1111/insr.12290