Publication
Data Integration by Combining Big Data and Survey Sample Data for Finite Population Inference
Publisher:
Wiley
Date:
12-2020
DOI:
10.1111/INSR.12434
Abstract: The statistical challenges in using big data for making valid statistical inference in the finite population have been well documented in literature. These challenges are due primarily to statistical bias arising from under‐coverage in the big data source to represent the population of interest and measurement errors in the variables available in the data set. By stratifying the population into a big data stratum and a missing data stratum, we can estimate the missing data stratum by using a fully responding probability s le and hence the population as a whole by using a data integration estimator. By expressing the data integration estimator as a regression estimator, we can handle measurement errors in the variables in big data and also in the probability s le. We also propose a fully nonparametric classification method for identifying the overlapping units and develop a bias‐corrected data integration estimator under misclassification errors. Finally, we develop a two‐step regression data integration estimator to deal with measurement errors in the probability s le. An advantage of the approach advocated in this paper is that we do not have to make unrealistic missing‐at‐random assumptions for the methods to work. The proposed method is applied to the real data ex le using 2015–2016 Australian Agricultural Census data.