统计学系系列讲座之326期

 

时间:2018年12月3日(星期一)10:30--11:30

地点:史带楼302室

主持人:夏寅 青年研究员 复旦大学管理学院统计学系

主题:Krigings Over Space and Time Based on Latent Low-Dimensional Structures

主讲人:张荣茂 教授  浙江大学

简介:张荣茂,浙江大学教授,博导,主要从事非平稳时间序列和高维空间数据的理论与应用研究,发表的杂志包括Ann. Statist.,J. Amer. Assoc. Statist., J. Econometrics等。2015年获浙江省杰出青年基金,主持国家自然科学基金和省部级基金项目多项。现任浙大统计所副所长,数据科学中心兼职教授,浙江省现场统计研究所副理事长,J. Korean Statist. Soc.(SCI期刊)和Intern. J. Math. Statist.编委。

 

摘要:We propose a new approach to represent nonparametrically the linear dependence structure of a spatio-temporal process in terms of latent common factors. Though it is formally similar to the existing reduced rank approximation methods (Section 7.1.3 of Cressie and Wikle, 2011),

the fundamental di_erence is that the low-dimensional structure is completely unknown in our setting, which is learned from the data collected irregularly over space but regularly over time. We do not impose any stationarity conditions over space either, as the learning is

facilitated by the stationarity in time. Krigings over space and time are carried out based on the learned low-dimensional structure. Their performance is further improved by a newly proposed aggregation method via randomly partitioning the observations accordingly to their

locations. A low-dimensional correlation structure also makes the krigings scalable to the cases when the data are taken over a large number of locations and/or over a long time period. Asymptotic properties of the proposed methods are established. Illustration with both simulated and real data sets is also reported.(A joint work with professors Yao, Q. W. and Huang, D.)

 

                                                           统计学系 

2018-11-28