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

• Published in: Statistical methods & applications Vol. 33; no. 2; pp. 555 - 580
• Main Authors: Golini, Natalia, Righi, Paolo
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 2024; Springer Nature B.V
• Subjects: Calibration; Chemistry and Earth Sciences; Computer Science; Data integration; Economics; Error correction; Estimation; Estimators; Finance; Health Sciences; Humanities; Insurance; Law; Management; Mathematics and Statistics; Medicine; Original Paper; Physics; Statistical analysis; Statistical Theory and Methods; Statistics; Statistics for Business; Statistics for Engineering; Statistics for Life Sciences; Statistics for Social Sciences; Big data; Calibration weighting; Model-based inference; Missing at random; Variance estimation; Data integration
• ISSN: 1618-2510; 1613-981X
• DOI: 10.1007/s10260-023-00740-y

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.