题目：Weak Separability Test for Spatial Functional Fields

主讲人：北京大学 姚方教授

主持人：西南财经大学统计学院 常晋源教授

时间：2020年5月15日（星期五）10:00-11:20

直播平台及会议ID：腾讯会议，218 871 600

报告摘要：

For analysis of spatial temporal data from a functional perspective, a heuristic extension of Karhunen-Loeve expansion is often used to decompose such data into temporal components and spatially correlated random fields. This structure provides a convenient tool to investigate the space-time interactions, but may not always hold for complex situations. In this work, we introduce a new concept of weak separability, and propose formal testing procedures to examine the validity of the heuristic Karhunen-Loeve decomposition. Asymptotic properties are studied to avoid using resampling procedures, e.g. bootstrap. Both parametric and nonparametric approaches are developed to estimate the asymptotic covariance by constructing lagged type estimators. We demonstrate the efficacy of our method via simulations, and illustrate the usefulness using two real examples: Harvard forest data and China PM2.5 data.

主讲人简介：

北京大学教授, 北大统计科学中心主任，数理统计学会（IMS）Fellow，美国统计学会（ASA）Fellow。2000年本科毕业于中国科学技术大学，2003取得加利福尼亚大学戴维斯分校统计学博士学位，曾任职于多伦多大学统计科学系终身教授。现担任Canadian Journal of Statistics主编，至今担任9个国际统计学核心期刊副主编/编委，包括统计学顶级期刊Journal of the American Statistical Association和Annals of Statistics。