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20190320 张新雨:Model Averaging Estimation for High-dimensional Covariance Matrix with a Network Structure
时间:2019-03-13

报告地点:明德主楼1016会议室

报告时间201932010:00-11:30

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:

张新雨,中科院数学与系统科学研究院预测中心副研究员,主要从事模型平均和模型选择方面的研究工作,在统计学四大期刊和计量经济学顶级期刊Journal of Econometrics发表论文十余篇。曾获优秀青年基金等三项自然科学基金委基金项目资助,目前担任两个SCl期刊编委和Econometrics客座主编。