统计与管理学院2017年学术报告第26期

【主  题】Model Averaging for Prediction with Fragmentary Data

【报告人】方方, 副教授

华东师范大学

【时  间】 2017年05月16日（星期二）14:00－15:00

【地  点】 上海财经大学统计与管理学院大楼1208室

【摘  要】One main challenge for statistical prediction with data from multiple sources is that not all the associated covariate data are available for many sampled subjects. Consequently, we need new statistical methodology to handle this type of ``fragmentary data" that has become more and more popular in recent years. In this paper, we propose a novel method based on frequentist model averaging that fits some candidate models using all available covariate data. The weights in model averaging are selected by delete-one cross-validation based on the data from complete cases. The optimality of the selected weights is rigorously proved under some conditions. The finite sample performance of the proposed method is confirmed by simulation studies. An example for personal income prediction based on real data from a leading e-community of wealth management in China is also presented for illustration.

【邀请人】 柏杨、黄涛