报告题目：Large matrix estimation for time series data
报告摘要：Motivated by the Markowitz optimal portfolio theory and the applications in other scientific fields, we consider the estimation of large covariance and precision matrices from high-dimensional observations with slowly decaying temporal dependence that is bounded by certain polynomial decay rate. 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 a gap-block cross-validation method is proposed for the tuning parameter selection, which performs well in simulations.