Integrating probability and big non-probability samples data to produce Official Statistics

Natalia Golini () and Paolo Righi ()
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Natalia Golini: University of Turin
Paolo Righi: Italian National Statistical Institute (Istat)

Statistical Methods & Applications, 2024, vol. 33, issue 2, No 7, 555-580

Abstract: Abstract This paper introduces the pseudo-calibration estimators, a novel method that integrates a non-probability sample of big size with a probability sample, assuming both samples contain relevant information for estimating the population parameter. The proposed estimators share a structural similarity with the adjusted projection estimators and the difference estimators but they adopt a different inferential approach and informative setup. The pseudo-calibration estimators can be employed when the target variable is observed in the probability sample and, in the non-probability sample, it is observed correctly, observed with error, or predicted. This paper also introduces an original application of the jackknife-type method for variance estimation. A simulation study shows that the proposed estimators are robust and efficient compared to the regression data integration estimators that use the same informative setup. Finally, a further evaluation using real data is carried out.

Keywords: Big data; Calibration weighting; Data integration; Missing at random; Model-based inference; Variance estimation (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10260-023-00740-y

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