Estimating General Parameters from Non-Probability Surveys Using Propensity Score Adjustment
Metadatos
Mostrar el registro completo del ítemEditorial
Mdpi
Materia
Nonprobability surveys Propensity score adjustment Survey sampling
Fecha
2020-11-23Referencia bibliográfica
Castro-Martín, L., Rueda, M. D. M., & Ferri-García, R. (2020). Estimating General Parameters from Non-Probability Surveys Using Propensity Score Adjustment. Mathematics, 8(11), 2096. [doi:10.3390/math8112096]
Patrocinador
Spanish Government MTM2015-63609-R; Instituto de Salud Carlos III Spanish Government PID2019-106861RB-I00-AEI-10.13039/501100011033Resumen
This study introduces a general framework on inference for a general parameter using
nonprobability survey data when a probability sample with auxiliary variables, common to both
samples, is available. The proposed framework covers parameters from inequality measures and
distribution function estimates but the scope of the paper is broader. We develop a rigorous
framework for general parameter estimation by solving survey weighted estimating equations
which involve propensity score estimation for units in the non-probability sample. This development
includes the expression of the variance estimator, as well as some alternatives which are discussed
under the proposed framework. We carried a simulation study using data from a real-world survey,
on which the application of the estimation methods showed the effectiveness of the proposed
design-based inference on several general parameters.