Many-to-One indirect sampling with application to the French postal traffic estimation

Anne Ruiz-Gazen ( Toulouse School of Economics), and Estelle Medous ( Toulouse School of Economics)

December 2, 2021, 11:00–12:15


Room Auditorium 5

MAD-Stat. Seminar


In social and economic surveys, it can be difficult to directly reach units of the target population, and indirect sampling is often advocated to solve this issue. In indirect sampling, the sample is drawn from a frame population that is linked to the target population, and estimation of target population parameters is typically achieved through the Generalized Weight Share Method (GWSM). This method provides a weight, for every unit of the target population, that depends on the one hand, on the sampling weights in the frame population and, on the other hand, on the link weights between the frame population and the target population. In this presentation, we focus on the situation in which the units from the frame population are linked to one and only one unit from the target population (Many-to-One case). This situation is encountered at the French postal service where addresses are sampled instead of postman rounds. We aim at understanding of the impact of the link weights on the efficiency of the GWSM estimators. We derive variance expressions and optimality results for a large class of sampling designs. Moreover, we note that the Many-to-One case can lead to too many links to observe. We alleviate the problem by introducing an intermediate population and double indirect sampling. The question of the loss of precision in this situation will be discussed through theoretical results and simulations. These findings help to explain the loss of precision of double GWSM estimators observed recently at the French postal service.