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Hierarchical Bayesian Markov switching models with application to predicting spawning success of shovelnose sturgeon

Journal of the Royal Statistical Society. Series C: Applied Statistics

By:
, , , ,
DOI: 10.1111/j.1467-9876.2008.00642.x

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Abstract

The timing of spawning in fish is tightly linked to environmental factors; however, these factors are not very well understood for many species. Specifically, little information is available to guide recruitment efforts for endangered species such as the sturgeon. Therefore, we propose a Bayesian hierarchical model for predicting the success of spawning of the shovelnose sturgeon which uses both biological and behavioural (longitudinal) data. In particular, we use data that were produced from a tracking study that was conducted in the Lower Missouri River. The data that were produced from this study consist of biological variables associated with readiness to spawn along with longitudinal behavioural data collected by using telemetry and archival data storage tags. These high frequency data are complex both biologically and in the underlying behavioural process. To accommodate such complexity we developed a hierarchical linear regression model that uses an eigenvalue predictor, derived from the transition probability matrix of a two-state Markov switching model with generalized auto-regressive conditional heteroscedastic dynamics. Finally, to minimize the computational burden that is associated with estimation of this model, a parallel computing approach is proposed. ?? Journal compilation 2009 Royal Statistical Society.

Additional Publication Details

Publication type:
Article
Publication Subtype:
Journal Article
Title:
Hierarchical Bayesian Markov switching models with application to predicting spawning success of shovelnose sturgeon
Series title:
Journal of the Royal Statistical Society. Series C: Applied Statistics
DOI:
10.1111/j.1467-9876.2008.00642.x
Volume
58
Issue:
1
Year Published:
2009
Language:
English
Larger Work Type:
Article
Larger Work Subtype:
Journal Article
First page:
47
Last page:
64
Number of Pages:
18