TY  - JOUR
CY  - Ottawa
ID  - publications48531
UR  - http://tse-fr.eu/pub/128953
IS  - n° 2
A1  - Medous, Estelle
A1  - Goga, Camelia
A1  - Ruiz-Gazen, Anne
A1  - Beaumont, Jean-François
A1  - Dessertaine, Alain
A1  - Puech, Pauline
N2  - 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.
VL  - vol. 49
TI  - QR prediction for statistical data integration
AV  - none
EP  - 410
Y1  - 2023/12//
PB  - Statistics Canada
JF  - Survey Methodology
KW  - Cosmetic estimator
KW  - Dual frame
KW  - GREG estimator
KW  - Non-probability sample
KW  - Probability sample
KW  - Variance estimator
SN  - 0714-0045
SP  - 385
ER  -