Population Empirical Likelihood Estimation in Dual Frame Surveys

Abstract

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.