An Asymptotic Theory of Least Squares Model Averaging
报告人： Fang Fang
Model averaging has attracted abundant attentions from researchers in the past decades as it becomes a powerful forecasting tool in areas such as econometrics, social sciences and medical studies. Theoretical results of model averaging methods mainly focus on asymptotical optimality of the selected model weights and large sample properties of the weights and the weighted parameter estimates. However, even with the most basic least squares model averaging, a full theoretical picture has not been obtained yet. In this talk, I will present some asymptotic results of least squares model averaging under two different scenarios: (1) All candidate models are wrong. (2) The true model is included in the candidate models. The results are connected to the asymptotical results of the model selection methods AIC and BIC. Some related issues will be further discussed.
About the Speaker:
Fang Fang is Associate Professor of Key Laboratory of Advanced Theory and Application in Statistics and Data Science - MOE, School of Statistics, East China Normal University. He got his Bachelor degree at Peking University in 2002 and Ph.D. degree at University of Wisconsin - Madison in 2007. His research interests include model averaging, statistical learning and multisource data analysis.