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Activity Number:
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109
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Type:
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Contributed
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Date/Time:
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Monday, July 30, 2007 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Bayesian Statistical Science
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| Abstract - #308576 |
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Title:
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Hierarchical Bayesian Markov Switching Models with Application to Predicting Spawning Success of Shovelnose Sturgeon
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Author(s):
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Ginger Davis*+ and Scott Holan and Mark Wildhaber
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Companies:
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University of Virginia and University of Missouri-Columbia and U.S. Geological Survey
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Address:
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151 Engineers Way, Charlottesville, VA, 22904-4747,
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Keywords:
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hierarchical ; Markov-switching ; longitudinal ; GARCH ; Bayesian ; eigenvalue
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Abstract:
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Spawning in sturgeon is linked to environmental patterns, rhythms, and cues. Due to the endangerment of sturgeon, efforts need to be made to increase recruitment. Little information is available on the biology and ecology of sturgeon to guide these efforts. It is not known where, when and under what conditions these species spawn in the Missouri River, and to what degree spawning is successful. Using measurements of biological variables associated with readiness to spawn as well as longitudinal behavioral data collected using telemetry and data storage device sensors, we introduce a hierarchical Bayesian model for predicting spawning success. This model uses an eigenvalue predictor from the transition probability matrix in a two-state Markov switching model with GARCH dynamics as a generated regressor in a linear regression model.
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