Model Averaging for Prediction with Fragmentary Data
时间：2016-12-15 10:00 ~ 11:00
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.
About the Speaker:
Dr. Fang Fang is Associate Professor in the School of Statistics at East China Normal University. He received his Ph.D. from the University of Wisconsin-Madison in 2007 and B.S. from Peking University in 2002. His research interests include missing data, model averaging, feature screening, fragmentary data analysis, and massive/stream data analysis. He has published research in top statistics journals such as the Annals of Statistics, Biometrika, and Statistica Sinica, and is an editorial board member of the Journal of Nonparametric Statistics.