学术报告

报告题目：A supplement on CLT for LSS under a large dimensional generalized spiked covariance model

报告时间：2019年6月17日15:30--16:30

报告地点：25教学楼14楼学术报告厅

摘要：Central limit theorem (CLT) for linear spectral statistics (LSSs) is widely used in large scale statistical inference when the sample size n and dimension p both tend to infinity. However, there always exists discrepancy between the sample mean and sample variance, and asymptotic mean and asymptotic variance when the CLT is applied for an LSS under spiked models. A major portion of the discrepancy is from the spiked eigenvalues, which depends on the dimensions (p; n) and the magnitudes of the spikes. In order to eliminate such discrepancy, we propose in this paper a supplement to the CLT defined as H_pCLT for a class of LSSs of sample covariance matrices. Simulation results demonstrate the success of the H_pCLT and exhibit its superiority to the original ones in various situations.

个人简介：张阳春，男，哈尔滨工业大学本硕博，研究方向：随机矩阵，极值特征值的研究等。2018.1—2019.1作为联合培养生在新加坡国立大学访问周望教授1年，2019.6.1—2019.6.14在香港科技大学数学系访学。