Estimation of Superpopulation Models for Surveys Under General Sampling Approaches.
(A2025-125869)

AUTHORS

Jose Vidal-Sanz, Universidad Carlos III de Madrid; Mercedes Esteban-Bravo, Universidad Carlos III de Madrid

ABSTRACT

This paper examines bias corrections for various parametric estimators, such as maximum likelihood estimators and Bayesian methods, when a sample is taken from a larger finite population using probabilistic sampling. It discusses the asymptotic properties of the estimators and covers a wide range of applications (such as nonresponses, and possible nonlinear regression models). Finally, we explore general corrections for non-probabilistic sampling methods, and simulations.