题目：Ultrahigh Dimensional Precision Matrix Estimation via Refitted Cross Validation and Application to Asset Allocation

报告人：李润泽教授

               （Pennsylvania State University, USA）

Abstract : This paper develops a new estimation procedure for ultrahigh dimensional sparse precision matrix, the inverse of covariance matrix. Regularization methods have been proposed for sparse precision matrix estimation, but they may not perform well with ultrahigh dimensional data due to spurious correlation. We propose a refitted cross validation (RCV) method for sparse precision matrix estimation based on its Cholesky decomposition. The proposed RCV procedure can be easily implemented with existing software for ultrahigh dimensional linear regression. We establish the consistency of the proposed RCV estimate and show that the rate of convergence of the RCV estimate without assuming banded structure is the same as those assuming the banded structure in Bickel and Levina (2008). Monte Carlo studies were conducted to access the finite sample performance of the RCV estimate. Our numerical comparison shows that the RCV estimate can outperform existing ones in various scenarios. We further apply the RCV estimate for an empirical analysis of asset allocation.

时间： 2019年6月13日（周四) 10:00-11:30

地点：565net必赢客户端本部教二楼513教室

联系人：崔恒建

欢迎全体师生积极参加！