时 间：2021年11月18日（周四）13:00-13:50
地 点：线上，腾讯会议305 493 290
题 目：Bayesian Empirical Likelihood Inference With Complex Survey Data
主讲人：赵普映 云南大学数学与统计学院副教授
主持人：唐炎林 研究员
摘 要：
We propose a Bayesian empirical likelihood approach to survey data analysis on a vector of finite population parameters defined through estimating equations. Our method allows overidentified 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 Bayesian inference proposed are established for both non-informative 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 Markov chain Monte Carlo procedure is described for the required computation of the posterior distribution for general vector parameters. Simulation studies and an application to a real survey data set are included to examine the finite sample performances of the methods proposed as well as the effect of different types of prior and different types of sampling design. This is a joint work with Malay Ghosh, J.N.K. Rao and Changbao Wu.
报告人简介：
赵普映，博士，云南大学数学与统计学院副教授、博士生导师，现主持国家自然科学基金面上项目1项。