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Activity Number:
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110
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Type:
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Contributed
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Date/Time:
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Monday, August 7, 2006 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Statistics and the Environment
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| Abstract - #306049 |
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Title:
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State-Space Models for within-Stream Network Dependence
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Author(s):
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William Coar*+ and F. Jay Breidt
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Companies:
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Colorado State University and Colorado State University
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Address:
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3400 Stanford Road, Fort Collins, CO, 80525,
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Keywords:
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Kalman filter ; Gaussian likelihood
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Abstract:
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Because of the natural flow of water in a stream network, characteristics of a downstream reach may depend on characteristics of upstream reaches. The flow of water from reach to reach provides a natural time-like ordering throughout the stream network. We propose a state-space model to describe the spatial dependence in this tree-like structure with ordering based on flow. The model formulation is flexible, allowing for a variety of spatial and temporal covariance structures in the state and measurement equations. A variation of the Kalman filter and smoother is derived to allow recursive estimation of unobserved states and prediction of missing observations on the network, as well as computation of the Gaussian likelihood. Several forms of dependence on the network are described, such as network analogues of autoregressive-moving average models and of local linear trend models.
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