High dimensional semiparametric estimate of latent covariance matrix for matrix-variate

发布时间：2018-03-16 作者：浏览次数：

Speaker:	赵俊龙	DateTime:	2018年3月17日（周六）上午8:30–9:30
Brief Introduction to Speaker: 赵俊龙，北京师范大学副教授		Place:	六号楼二楼报告厅
Abstract:Estimation of the covariance matrix of high dimensional matrix-variate is an important issue. Many methods have been developed, based on sample covariance matrix under the Gaussian or sub-Gaussian assumption. However, sub-Gaussian assumption is restrictive and the estimate based on the sample covariance matrix is not robust. In this paper, we consider the estimate of covariance matrix for high dimensional matrix-variate in the frame of transelliptical distribution and the Kendall's $\tau$ correlation. Since the covariance matrix of matrix-variate is commonly assumed to own some low dimension structure, we consider the structure of Kronecker expansion in this paper. The asymptotic results of the estimator are established. Simulation results and real data analysis confirm the effectiveness of our method.

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