报告题目：A Sumca Approach to Measures of Uncertainty for Complex Inference in Surveys
报 告 人：Jiming Jiang 教授，University of California，Davis
报告时间：2019年7月31日 9：00-10：00
报告地点：数学楼一楼第二报告厅
报告摘要：
We propose a simple, unified, Monte-Carlo assisted (Sumca) approach to second-order unbiased estimation of mean squared prediction error (MSPE) of a small area predictor. The proposed MSPE estimator is easy to derive, has a simple expression, and applies to a broad range of predictors that include the traditional empirical best linear unbiased predictor (EBLUP), empirical best predictor (EBP), and post model selection EBLUP and EBP as special cases. Furthermore, the leading term of the proposed MSPE estimator is guaranteed positive; the lower-order term corresponds to a bias correction, which can be evaluated via a Monte-Carlo method. The computational burden for the Monte-Carlo evaluation is much lesser, compared to other Monte-Carlo based methods that have been used for producing second-order unbiased MSPE estimators, such as double bootstrap and Monte-Carlo jackknife. The Sumca estimator also has a nice stability feature. Theoretical and empirical results demonstrate properties and advantages of the Sumca estimator.
报告人简介：
Jiming Jiang(蒋继明)，加利福尼亚大学戴维斯分校统计系教授。申请人主要研究领域为混合效应模型、小区域估计、模型选择、大数据智能、统计遗传学。多次受邀参加相关领域的国际学术会议并作大会主旨报告，先后被选为Institute of Mathematical Statistics 及 American Statistical Association 等国际著名统计协会的Fellow，并长期担任Journal of American Statistical Association、The Annals of Statistics等学术期刊的副主编，曾获得美国统计协会的Outstanding Statistical Application奖等。
