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Activity Number: 410
Type: Contributed
Date/Time: Tuesday, July 31, 2012 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics and the Environment
Abstract - #304153
Title: Spatio-Temporal Models for Some Data Sets in Continuous Space and Discrete Time
Author(s): Samuel Demel*+ and Juan Du
Companies: Kansas State University and Kansas State University
Address: Department of Statistics, Manhattan, KS, 66506, United States
Keywords: Autoregressive and moving average process (ARMA) ; Covariance ; Spatio-temporal covariance function ; Spatio-temporal covariance function ; Spatial statistics ; Fourier transform

Space time data sets are often collected at monitored discrete time lags, which are normally viewed as a component of time series. Valid and practical covariance structures are needed to model these types of data sets in various disciplines, such as environmental science, climatology and agriculture. In this work we propose two classes of spatio-temporal functions whose discrete temporal margins are some celebrated autoregressive and moving average (ARMA) models, and obtain necessary and sufficient conditions for them to be spatio-temporal covariance functions. The possibility of taking the advantage of well-established time series and spatial statistics tools makes it relatively easy to identify and fit the proposed model in practice. Simulation study is conducted to illustrate the application of the proposed model for estimation or prediction, comparing with some general existing parametric models in terms of likelihood and mean squared prediction error. A spatio-temporal model with AR(2) discrete temporal margin is fitted to wind data from Ireland

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