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Full-Text Articles in Physical Sciences and Mathematics
Bayesian Hierarchical Analysis Of Minefield Data, Noel A. Cressie, Andrew B. Lawson
Bayesian Hierarchical Analysis Of Minefield Data, Noel A. Cressie, Andrew B. Lawson
Professor Noel Cressie
Based on remote sensing of a potential minefield, point locations are identified, some of which may not be mines. The mines and mine-like objects are to be distinguished based on their point patterns, although it must be emphasized that all we see is the superposition of their locations. In this paper, we construct a hierarchical spatial point-process model that accounts for the different patterns of mines and mine-like objects and uses posterior analysis to distinguish between them. Our Bayesian approach is applied to COBRA image data obtained from the NSWC Coastal Systems Station, Dahlgren Division, Panama City, Florida. 2003 Copyright …
Fast, Resolution-Consistent Spatial Prediction Of Global Processes From Satellite Data, Hsin-Cheng Huang, Noel A. Cressie, John Gabrosek
Fast, Resolution-Consistent Spatial Prediction Of Global Processes From Satellite Data, Hsin-Cheng Huang, Noel A. Cressie, John Gabrosek
Professor Noel Cressie
Polar orbiting satellites remotely sense the earth and its atmosphere, producing datasets that give daily global coverage. For any given day, the data are many and measured at spatially irregular locations. Our goal in this article is to predict values that are spatially regular at different resolutions; such values are often used as input to general circulation models (GCMs) and the like. Not only do we wish to predict optimally, but because data acquisition is relentless, our algorithm must also process the data very rapidly. This article applies a multiresolution autoregressive tree-structured model, and presents a new statistical prediction methodology …
Data Mining Of Misr Aerosol Product Using Spatial Statistics, Tao Shi, Noel A. Cressie
Data Mining Of Misr Aerosol Product Using Spatial Statistics, Tao Shi, Noel A. Cressie
Professor Noel Cressie
In climate models, aerosol forcing is the major source of uncertainty in climate forcing, over the industrial period. To reduce this uncertainty, instruments on satellites have been put in place to collect global data. However, missing and noisy observations impose considerable difficulties for scientists researching global aerosol distribution, aerosol transportation, and comparisons between satellite observations and global-climate-model outputs. In this paper, we propose a Spatial Mixed Effects (SME) statistical model to predict the missing values, denoise the observed values, and quantify the spatial-prediction uncertainties. The computations associated with the SME model are linear scalable to the number of data points, …
Fixed Rank Filtering For Spatio-Temporal Data, Noel Cressie, Tao Shi, Emily L. Kang
Fixed Rank Filtering For Spatio-Temporal Data, Noel Cressie, Tao Shi, Emily L. Kang
Professor Noel Cressie
Datasets from remote-sensing platforms and sensor networks are often spatial, temporal, and very large. Processing massive amounts of data to provide current estimates of the (hidden) state from current and past data is challenging, even for the Kalman filter. A large number of spatial locations observed through time can quickly lead to an overwhelmingly high-dimensional statistical model. Dimension reduction without sacrificing complexity is our goal in this article. We demonstrate how a Spatio-Temporal Random Effects (STRE) component of a statistical model reduces the problem to one of fixed dimension with a very fast statistical solution, a methodology we call Fixed …