NDLI: Dynamic multi-resolution spatial models

Content Provider	Springer Nature Link
Author	Johannesson, Gardar Cressie, el Huang, Hsin Cheng
Copyright Year	2007
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.
Starting Page	5
Ending Page	25
Page Count	21
File Format	PDF
ISSN	13528505
Journal	Environmental and Ecological Statistics
Volume Number	14
Issue Number	1
e-ISSN	15733009
Language	English
Publisher	Kluwer Academic Publishers-Plenum Publishers
Publisher Date	2007-01-20
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Bayesian models Change-of-resolution Kalman filter Spatio-temporal prediction Total column ozone Tree-structured model Evolutionary Biology Mathematical Biology in General Statistics Ecology
Content Type	Text
Resource Type	Article
Subject	Statistics and Probability Environmental Science Statistics, Probability and Uncertainty

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