Dynamic multi-resolution spatial models |
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Authors: | Gardar Johannesson Noel Cressie Hsin-Cheng Huang |
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Institution: | (1) Lawrence Livermore National Laboratory, Livermore, CA 94550, USA;(2) Department of Statistics, The Ohio State University, Columbus, OH 43210, USA;(3) Institute of Statistical Science, Academia Sinica, Taipei, 115, Taiwan, Republic of China |
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Abstract: | Data from remote-sensing platforms play an important role in monitoring environmental processes, such as the distribution
of stratospheric ozone. Remote-sense data are typically spatial, temporal, and massive. Existing prediction methods such as
kriging are computationally infeasible. The multi-resolution spatial model (MRSM) captures nonstationary spatial dependence
and produces fast optimal estimates using a change-of-resolution Kalman filter. However, past data can provide valuable information
about the current status of the process being investigated. In this article, we incorporate the temporal dependence into the
process by developing a dynamic MRSM. An application of the dynamic MRSM to a month of daily total column ozone data is presented,
and on a given day the results of posterior inference are compared to those for the spatial-only MRSM. It is apparent that
there are advantages to using the dynamic MRSM in regions where data are missing, such as when a whole swath of satellite
data is missing. |
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Keywords: | Bayesian models Change-of-resolution Kalman filter Spatio-temporal prediction Total column ozone Tree-structured model |
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