Enhancing Multiple Frame Surveys: Improved Calibration and Efficient Bootstrap Techniques

Abstract

In recent years, multiple frame surveys have gained significant attention due to their applicability in capturing special or challenging-to-sample populations. This paper introduces two methodological advancements, the calibrated multiplicity estimator and without-replacement bootstrap techniques, in the field of multiple frame surveys. A comprehensive simulation study assesses their performance. The calibrated multiplicity estimator is demonstrated to outperform the multiplicity estimator, particularly in terms of mean squared error, with a ratio ranging from 0.6 to 0.8. Furthermore, the study shows that without-replacement bootstrap techniques perform favorably compared to their with-replacement counterparts. Future research directions include conducting more extensive simulations with real-world data and establishing the theoretical properties of the proposed estimator. This paper contributes to the growing body of knowledge on multiple frame surveys and their estimation methods.