報告題目：A Sumca Approach to Measures of Uncertainty for Complex Inference in Surveys
報 告 人：Jiming Jiang 教授，University of California，Davis
報告時間：2019年7月31日 9：00-10：00
報告地點：數學樓一樓第二報告廳
報告摘要：
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.
報告人簡介：
Jiming Jiang(蔣繼明)，加利福尼亞大學戴維斯分校統計系教授。申請人主要研究領域為混合效應模型、小區域估計、模型選擇、大數據智能、統計遺傳學。多次受邀參加相關領域的國際學術會議并作大會主旨報告，先後被選為Institute of Mathematical Statistics 及 American Statistical Association 等國際著名統計協會的Fellow，并長期擔任Journal of American Statistical Association、The Annals of Statistics等學術期刊的副主編，曾獲得美國統計協會的Outstanding Statistical Application獎等。
