报告简介  Abstract

Estimation of high-dimensional banded covariance matrix is widely used in multivariate statistical analysis. To ensure the validity of estimation, we aim to test the hypothesis that the covariance matrix is banded with a certain bandwidth under the high-dimensional framework. Though several testing methods have been proposed in the literature, the existing tests are only powerful for some alternatives with certain sparsity levels, whereas they may not be powerful for alternatives with different sparsity levels. The goal of this paper is to propose a new test for banded covariance matrix which is powerful for alternatives with various sparsity levels. The proposed new test can also be used for testing the banded structure of covariance matrices of error vectors in high-dimensional factor models. Simulation studies and an application to a prostate cancer data from protein mass spectroscopy are conducted for evaluating the validity of the proposed new test for banded covariance matrix.

嘉宾简介  About the Speaker

郑术蓉，东北师范大学数学与统计学院教授。主要研究方向是：大维随机矩阵理论及高维统计分析。曾在Annals of Statistics, JASA, Biometrika等统计学期刊上发表多篇跟大维随机矩阵理论有关的学术论文。现任Statistica Sinica, Journal of Multivariate Analysis等学术期刊编委，全国青年统计学家协会副会长等。曾主持国家自然科学基金委优秀青年科学基金、面上项目等多个项目。

讲座海报Poster