Inference under unequal probability sampling with the Bayesian exponentially tilted empirical likelihood.

ABSTRACT: Fully Bayesian inference in the presence of unequal probability sampling requires stronger structural assumptions on the data-generating distribution than frequentist semiparametric methods, but offers the potential for improved small-sample inference and convenient evidence synthesis. We demonstrate that the Bayesian exponentially tilted empirical likelihood can be used to combine the practical benefits of Bayesian inference with the robustness and attractive large-sample properties of frequentist approaches. Estimators defined as the solutions to unbiased estimating equations can be used to define a semiparametric model through the set of corresponding moment constraints. We prove Bernstein-von Mises theorems which show that the posterior constructed from the resulting exponentially tilt

SUBMITTER: Yiu A 

PROVIDER: S-EPMC7612173 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature