RT Journal Article
SR 00
ID ISSN: 0714-0045
A1 Medous, Estelle
A1 Goga, Camelia
A1 Ruiz-Gazen, Anne
A1 Beaumont, Jean-François
A1 Dessertaine, Alain
A1 Puech, Pauline
T1 QR prediction for statistical data integration
JF Survey Methodology
YR 2023
FD 2023-12
VO vol. 49
IS n° 2
SP 385
OP 410
K1 Cosmetic estimator
K1 Dual frame
K1 GREG estimator
K1 Non-probability sample
K1 Probability sample
K1 Variance estimator
AB In this paper, we investigate how a big non-probability database can be used to improve estimates of finite population totals from a small probability sample through data integration techniques. In the situation where the study variable is observed in both data sources, Kim and Tam (2021) proposed two design-consistent estimators that can be justified through dual frame survey theory. First, we provide conditions ensuring that these estimators are more efficient than the Horvitz-Thompson estimator when the probability sample is selected using either Poisson sampling or simple random sampling without replacement. Then, we study the class of QR predictors, introduced by Särndal and Wright (1984), to handle the less common case where the non-probability database contains no study variable but auxiliary variables. We also require that the non-probability database is large and can be linked to the probability sample. We provide conditions ensuring that the QR predictor is asymptotically design-unbiased. We derive its asymptotic design variance and provide a consistent design-based variance estimator. We compare the design properties of different predictors, in the class of QR predictors, through a simulation study. This class includes a model-based predictor, a model-assisted estimator and a cosmetic estimator. In our simulation setups, the cosmetic estimator performed slightly better than the model-assisted estimator. These findings are confirmed by an application to La Poste data, which also illustrates that the properties of the cosmetic estimator are preserved irrespective of the observed non-probability sample.
PB Statistics Canada
SN 0714-0045
LK https://publications.ut-capitole.fr/id/eprint/48531/
UL http://tse-fr.eu/pub/128953