报告人：南斌（department of bioinformatics University of California at Irvine）

  

时间：4月4日（星期四）下午15:00--16:00

  

地点：苏州大学本部精正楼211

  

Abstract: We consider the estimation of large covariance and precision matrices from high-dimensional sub-Gaussian or heavier-tailed observations with slowly decaying temporal dependence. The temporal dependence is allowed to be long-range so with longer memory than those considered in the current literature. We show that several commonly used methods for independent observations can be applied to the temporally dependent data. The rates of convergence are obtained, and the properties of sparsistency and sign-consistency are also established. A gap-block cross-validation method is proposed for the tuning parameter selection, which performs well in simulations.  As a motivating example, we study the brain functional connectivity using resting-state fMRI time series data with long-range temporal dependence. This is a joint work with Hai Shu.
Bio: Dr. Bin Nan is Professor of Statistics in the School of Information and Computer Sciences at the University of California, Irvine. He received his Ph.D. in Biostatistics from the University of Washington in 2001 and joined the faculty in the Department of Biostatistics at the University of Michigan in the same year. He moved to UC Irvine in 2017. He has broad interests in statistics and biostatistics with applications in biomedical research. His current interests are focusing on the development of new methods in the areas of survival analysis, analysis of high-dimensional brain image data, and analysis of longitudinal data with change-points, terminal events, and variables subject to limit of detection. He is Fellow of the American Statistical Association, Fellow of the Institute of Mathematical Statistics, and Elected Member of International Statistical Institute. He is currently Associate Editor of the Journal of the American Statistical Association-A&CS, Lifetime Data Analysis, and Statistics in Biosciences.