報告人: 張新雨 研究員

講座日期：2019-12-11

講座時間：10:00

報告地點：長安校區(qū) 數(shù)學與信息科學學院學術(shù)交流廳

主辦單位：數(shù)學與信息科學學院

講座人簡介：

張新雨，中科院系統(tǒng)所/預測中心研究員，Texas A&M大學博士后、Penn State 大學Research Fellow。主要研究方向為模型平均、模型選擇、組合預測等。先后主持杰青、優(yōu)青等4項國家自然科學基金，目前擔任《JSSC》、《SADM》、《系統(tǒng)科學與數(shù)學》、《應用概率統(tǒng)計》編委和《Econometrics》客座主編。

講座簡介：

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.