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報告題目：Assessing Uncertainty in Small Area Estimation under A Misspecified Model

主講人：蔣繼明教授（加州大學戴維斯分校）

時間：2024年7月12日（周五）15:30 p.m.

地點：北院卓遠樓305會議室

主辦單位：統計與數學學院

摘要：Observed best prediction (OBP) is a method of small area estimation that is known to be more robust against model misspecification than the traditional empirical best linear unbiased prediction method. However, assessing uncertainty in OBP has been a difficult task due to the potential model misspecification. This is because the assumed model cannot be used in deriving an uncertainty measure for OBP (otherwise, it would defeat the whole purpose of OBP). This talk provides an overview of a series of methods that have been developed for estimating the mean squared prediction error of OBP. Most of the developments focus on area-level models, where the robustness of OBP is well established. The models under consideration include the Fay-Herriot model and Poisson/gamma model for small area counts. Examples of empirical studies and real-data applications are discussed. This work is joint with Xiaohui Liu, Haiqiang Ma of Jiangxi University of Finance and Economics, China and Thuan Nguyen of Oregon Health and Science University, USA.

主講人簡介：

蔣繼明，現為加州大學戴維斯分校的統計學教授, 統計系系主任。其研究興趣包括混合效應模型、模型選擇、小區域估計、縱向數據分析、精準醫療、大數據智能、隱私保護、統計遺傳學/生物信息學、年齡標準化癌癥率以及漸近理論；發表研究論文超過100篇，其中多篇刊在AoS、JASA、JRSSB和Biometrika等頂級統計與數據科學期刊上；先后出版了五本專著，包括《Generalized Linear Mixed Models and Their Applications》、《Large Sample Techniques for Statistics》、《The Fence Methods》等。蔣繼明教授是AoS和JASA等多個統計學國際期刊的編委，是美國科學促進會（AAAS）、美國統計協會（ASA）、國際數理統計學會（IMS）的Fellow，也是國際統計學會（ISI）的Elected Member。