報告題目： Model averaging prediction for time series models with a diverging number of parameters
報 告 人：Guohua Zou 教授 首都師範大學
報告時間：2019年7月11日上午10:40-11:20
報告地點：數學樓一樓第一報告廳
報告摘要:
An important problem with model averaging approach is the choice of weights. In this paper, a generalized Mallows model averaging (GMMA) criterion for choosing weights is developed in the context of an infinite order autoregressive (AR(infinity)) process. The GMMA method adapts to the circumstances in which the dimensions of candidate models can be large and increase with the sample size. The GMMA method is shown to be asymptotically optimal in the sense of obtaining the best out-of-sample mean-squared prediction error (MSPE) for both the independent-realization and the same-realization predictions, which, as a byproduct, solves a conjecture put forward by Hansen (2008) that the well-known Mallows model averaging (MMA) criterion from Hansen (2007) is asymptotically optimal for predicting the future of a times series. The rate of the GMMA based weight estimator tending to the optimal weight vector minimizing the independent-realization MSPE is derived as well. Both simulation experiment and real data analysis illustrate the merits of GMMA method in the prediction of AR(infinity) process.
報告人簡介：
Zou教授于1995年獲得中國科學院系統科學研究所統計學博士學位。他獲得國家傑出青年科學基金項目資助。 他的主要研究興趣包括利用統計理論和方法來分析實際的經濟，醫學和遺傳數據。他的研究領域包括統計模型選擇和平均，調查抽樣，統計決策理論和統計遺傳學。 他特别關注的是混合效應模型，預測試估計和計量經濟學預測因子和測試的敏感性，估計量和預測因子的最優性，如可接受性和極小性，調查中的設計和數據分析，以及疾病和基因之間的聯系和關聯研究。
