This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
Activity Number:
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525
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Type:
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Contributed
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Date/Time:
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Wednesday, August 4, 2010 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Statistics and the Environment
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Abstract - #308907 |
Title:
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Online Forecasting and Prediction of Spacio-Temporal Processes with Dynamic Covariance Estimation
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Author(s):
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Dave Zes*+
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Companies:
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University of California, Los Angeles
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Address:
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3134 Kallin Ave, Long Beach, CA, 90808, United States
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Keywords:
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geostatistics ;
covariogram ;
kriging ;
state-space ;
online estimation ;
statistical learning
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Abstract:
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In the present work we advocate a facile state-space formulation for spacio-temporal data. We assume an underlying deterministic linear process, whose coefficients are low variance states. Solving the system is reminiscent of statistical learning, where a model is "hyperparameterized" through training. Online estimation as well as retrospective analysis are much more computationally efficient than, e.g., hierarchical models, which require sequential re-fitting for state estimation -- an attractive advantage in light of the increasing availability of large geo-statistical data sets.
Method comparisons from climatological and ecological data suggest excellent performance. Extensions include non-parametric one- and two-surface covariograms, forecasting to known locations, smoothing, prediction to arbitrary locations, and moving sites. We also include video resources for illustration.
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Authors who are presenting talks have a * after their name.
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