报告时间：2019年5月15日 10:00—11:00
报告地点：明德主楼1016会议室
报告主题：Model averaging prediction for time series models with a diverging number of parameters
报告摘要：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.
报告人简介：邹国华，博士毕业于中国科学院系统科学研究所。主要从事统计学的理论研究及其在经济金融、生物医学中的应用研究工作，在统计模型选择与平均、决策函数的优良性、抽样调查的设计与分析、疾病与基因的关联分析等方面的研究中取得了一系列重要成果，得到了国内外同行的好评与肯定，并被广泛引用。共出版教材1本，在《中国科学》（5篇）、Biometrika（2篇）、Genetics（3篇）、Journal of Econometrics（6篇）、Journal of the American Statistical Association（4篇）等国内外顶级期刊上发表论文110余篇；主持或参加过二十多项国家自然科学基金项目以及全国性的实际课题，提出的预测方法被实际部门所采用。