题目：Sample Empirical Likelihood Inference With Complex Survey Data

主讲人：云南大学 赵普映教授

主持人：西南财经大学 常晋源教授

时间：2024年3月7日 （周四）上午9:30-12:00 下午14:00-16:30

地点：西南财经大学光华校区光华楼10楼1003

报告摘要：

The sample empirical likelihood approach provides a powerful tool for analysis of complex survey data. We present results of sample empirical likelihood for point estimation and linear or nonlinear hypothesis tests on finite population parameters defined through just-identified or over-identified estimating equation systems with smooth or non-differentiable estimating functions under general unequal probability sampling designs. We propose an augmented estimating equations method with nuisance functionals and complex surveys. The second-step augmented estimating functions automatically handle the impact of the first-step plug-in estimator, and the resulting estimator of the main parameters of interest is invariant to the first step method. More importantly, the generalized empirical likelihood-based Wilks’ theorem holds for the main parameters of interest under the design-based framework for commonly used survey designs, and the maximum generalized empirical likelihood estimators achieve the semiparametric efficiency bound. 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 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.

主讲人简介：

赵普映，博士，云南大学数学与统计学院教授、博士生导师，兼任云南省应用统计学会理事长。主要从事缺失数据统计分析、复杂调查数据分析的研究工作。主持国家自然科学基金面上项目1项、重点项目子项目1项，云南省杰出青年科学基金项目1项。获第九届高等学校科学研究优秀成果奖（人文社会科学）青年成果奖、云南省自然科学奖一等奖，入选云南省“高层次人才引进计划”青年人才专项。