报告题目：Double-Robust Small Area Estimation

主讲人：马海强副教授(江西财经大学)

时间：2026年9月15日（周二）9:00 a.m.

形式：线上讲座

腾讯会议：324-785-983

主办单位：统计与数学学院

摘要：

In the context of robust small area estimation (SAE), there are two types of robustness considerations,robustness against model misspecification and robustness against outliers. We propose a method of SAEthat has both types of robustness features. The method combines the idea of observed best prediction (OBP),which is known to be more robust against model misspecification than the traditional best linear unbiased prediction (EBLUP) method, and the method of density power divergence (DPD), which is known to be more robust against outliers than the EBLUP. The double robust predictor (DRP) is developed under an area-levelmodel with normal or normal-mixture sampling errors, and under a unit-level model. Another advantageof the DRP method is that it provides an natural estimator of a tuning parameter involved in the DPD. Wedevelop theory about the proposed method, and demonstrate empirical performance of the proposed DRPand its comparison to EBLUP, OBP, a robust version of the EBLUP, and predictors based on the DPD. A second order unbiased estimator of the mean squared prediction error of the DRP is developed and its performance is evaluated.

主讲人简介：

马海强，江西财经大学统计与数据科学学院副教授，博士生导师，主要的研究方向有：函数型数据分析，混合效应模型，稳健统计分析。目前，已在国内外学术期刊《Journal of the American Statistical Association》、《Statistics Sinica》、《TEST》、《中国科学：数学》、《数学学报》等国内外权威期刊发表学术论文三十多篇，兼任中国现场研究会资源与环境分会理事、全国工业统计研究会理事、全国工业统计研究会青年统计学家协会理事，先后主持国家自然科学基金面上项目、国家自然科学基金青年项目、国家自然科学基金地区项目各 1 项，主持中国博士后面上项目 1 项，江西省自然科学基金重点项目 1 项，江西省教育厅科技项目 1 项以及江西高校人文社科项目等省级项目多项，参与科技部重点研发项目，国家社科基金重大项目，国家自然科学基金面上项目多项。