Population Empirical Likelihood Estimation in Dual Frame Surveys
Identificadores
URI: http://hdl.handle.net/10481/68523Metadatos
Afficher la notice complèteMateria
Multiplicity Auxiliary information Multiple frame surveys Finite population inference
Date
2020-08-05Referencia bibliográfica
del Mar Rueda, M., Ranalli, M.G., Arcos, A. et al. Population empirical likelihood estimation in dual frame surveys. Stat Papers (2020). https://doi.org/10.1007/s00362-020-01200-5
Patrocinador
Ministerio de Economía y Competitividad of SpainRésumé
Dual frame surveys are a device to reduce the costs derived from data
collection in surveys and improve coverage for the whole target population. Since
their introduction, in the 1960’s, dual frame surveys have gained much attention
and several estimators have been formulated based on a number of different approaches. In this work, we propose new dual frame estimators based on the population empirical likelihood method originally proposed by Chen and Kim (2014) and
using both the dual and the single frame approach. The extension of the proposed
methodology to more than two frame surveys is also sketched. The performance
of the proposed estimators in terms of relative bias and relative mean squared
error is tested through simulation experiments. These experiments indicate that
the proposed estimators yield better results than other likelihood-based estimators
proposed in the literature.