报告题目：Optimal Weight Choice for Frequentist Model Average Estimators

时间：2015年5月27（星期三）13：00-14：00

地点：学院南路校区，学术会堂603

报告人：Professor Hua Liang, Department of Statistics, The George Washington University

摘要：

There has been increasing interest recently in model averaging within the frequentist paradigm. The main benefit of model averaging over model selection is that it incorporates rather than ignores the uncertainty inherent in the model selection process. One of the most important, yet challenging, aspects of model averaging is how to optimally combine estimates from different models. In this work, we suggest a procedure of weight choice for frequentist model average estimators that exhibits optimality properties with respect to the estimator's mean squared error (MSE). As a basis for demonstrating our idea, we consider averaging over a sequence of linear regression models. Building on this base, we develop a model weighting mechanism that involves minimizing the trace of an unbiased estimator of the model average estimator's MSE. We further obtain results that reflect the finite sample as well as asymptotic optimality of the proposed mechanism. A Monte Carlo study based on simulated and real data evaluates and compares the finite sample properties of this mechanism with those of existing methods. The extension of the proposed weight selection scheme to general likelihood models is also considered.

报告人简介：

梁华教授为中央财经大学“手拉手”项目特聘教授，美国乔治华盛顿大学教授，中科院数理统计博士，Texas A&M University统计学博士。出版学术著作2部, 学术论文120多篇，其中20篇发表在国际统计学最顶级的四个期刊上（The Annals of Statistics, Biometrika, JASA, and JRSSB)。梁华教授共主持了6项美国国家科学基金会以及美国国立卫生研究院的研究项目，研究经费总计250万美元。另外还主持了1项海外港澳学者研究基金。他是美国统计学会(ASA), 国际数理统计学会(IMS), 英国皇家统计学会(RSS)会员。同时担任Journal of the American Statistical Association等多个国际著名统计学期刊的副主编。