时 间：2020年9月1日(周二)下午15:30-17:00
地 点： 腾讯会议ID：578 184 484
题 目：Weak Separability Test for Spatial Functional Fields
主讲人：姚方 北京大学讲席教授, 北大统计科学中心主任
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.
报告人简介：
北京大学讲席教授, 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技大学统计专业，2003获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的主编，至今担任9个国际统计学核心期刊编委，包括统计学顶级期刊Journal of the American Statistical Association和 Annals of Statistics。
