报告时间：2019年3月20日 10:00-11:30
报告题目: Model Averaging Estimation for High-dimensional Covariance Matrix
with a Network Structure
报告摘要:
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.
报告人简介：