Statistics Seminar（2016-04）

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

Speaker:Qiwei Yao,London School of Economics and Political Science

Time:Thursday, 14 April, 14:00-15:00

Place:Room 217, Guanghua Building 2

Abstract:

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.

Introduction:

Qiwei Yao is a Professor of Statistics at Department of Statistics, London School of Economics and Political Science. He is a Distinguished Visiting Professor at Guanghua School of Management, Peking University since 2004. His Current Research Interests are: Time series analysis; high-dimensional time series modeling and forecasting; dimension reduction and factor modelling; dynamical network modelling; spatiotemporal; modelling; financial econometrics; nonparametric regression.

//stats.lse.ac.uk/q.yao/

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