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A0302
Title: Bayesian empirical likelihood inference with complex survey data Authors:  Puying Zhao - Yunnan University (China) [presenting]
Abstract: A Bayesian empirical likelihood approach is proposed to survey data analysis on a vector of finite population parameters defined through estimating equations. The method allows over-identified estimating equation systems and is applicable to both smooth and nondifferentiable estimating functions. Our proposed Bayesian estimator is design-consistent for general sampling designs and the Bayesian credible intervals are calibrated in the sense of having asymptotically valid design-based frequentist properties under single-stage unequal probability sampling designs with small sampling fractions. Large sample properties of the proposed Bayesian inference are established for both noninformative and informative priors under the design-based framework. We also propose a Bayesian model selection procedure with complex survey data and show that it works for general sampling designs. An efficient MCMC procedure is described for the required computation of the posterior distribution for general vector parameters. Simulation studies and an application to a real survey dataset are included to examine the finite sample performances of the proposed methods as well as the impact of different types of priors and different types of sampling designs.