题目：Spatio-temporal Expanding Distance Asymptotic Framework for Locally Stationary Processes

主讲人：科罗拉多州立大学统计系 王昊南教授

主持人：西南财经大学统计学院 常晋源教授

时间：2019年7月19日（星期五）15:00-16:00

地点：西南财经大学光华校区光华楼1007会议室

报告摘要：

Spatio-temporal data indexed by sampling s and sampling time points are encountered in many scientific disciplines such as climatology, environmental sciences, and public health. We propose a novel spatio-temporal expanding distance (STED) asymptotic framework for studying the properties of statistical inference for nonstationary spatio-temporal models. In particular, to model spatio-temporal dependence, we develop a new class of locally stationary spatio-temporal covariance functions. The STED asymptotic framework has a fixed spatio-temporal domain for spatio-temporal processes that are globally nonstationary in a rescaled fixed domain and locally stationary in a distance expanding domain. The utility of STED is illustrated by establishing the asymptotic properties of the maximum likelihood estimation for a general class of spatio-temporal covariance functions, as well as a simulation study which suggests sound finite-sample properties. This is the joint work with Jialuo Liu, Tingjin Chu and Jun Zhu.

主讲人简介：

Haonan Wang received his Ph.D. degree in statistics from the University of North Carolina at Chapel Hill in 2003. Currently, he is a Professor of Statistics at Colorado State University. His research interests include object oriented data analysis, functional dynamic modeling of neuron activities, spatial and spatio-temporal modeling, and statistical learning for big data.