讲座内容简介：In this paper, we develop a model averaging method to estimate the high-dimensional covariance matrix, where the candidate models are constructed by different orders of the polynomial functions. We propose a Mallows-type model averaging criterion and select the weights by minimizing this criterion, which is an unbiased estimator of the expected in-sample squared error plus a constant. Then, we prove the asymptotic optimality of the resulting model average covariance (MAC) estimators. Furthermore, numerical simulations and a case study on Chinese airport network structure data are conducted to demonstrate the usefulness of the proposed approaches.

讲座人简介：张新雨，中科院系统所/预测中心研究员，Texas A&M大学博士后、Penn State 大学Research Fellow。主要研究方向为模型平均、模型选择、组合预测等。先后主持杰青、优青等4项国家自然科学基金，目前担任《JSSC》、《SADM》、《系统科学与数学》、《应用概率统计》编委和《Econometrics》客座主编。