主  題：Kriging Over Space and Time Based on a Latent Reduced Rank Structure

內容簡介：We propose a new approach to extract nonparametrically covariance 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 difference is that the low-dimensional structure is completely unknown in our setting, which is learned from the data collected irregularly over space but regularly in 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 accordinly to their locations.  A low-dimensional correlation structure also makes the kriging methods 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.
報告人：姚琦偉     教授    博導

                      中國“”專家

                      北京大學光華管理學院特聘教授

                      香港大學統計與精算學系名譽教授

                      英國皇家統計學會名譽會員

時  間：2016-07-29    09:30

地  點：競慧西樓402

舉辦單位：理學院  統計與大數據科學研究院  科研部