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


  • Scott H. Holan
  • Ginger M. Davis
  • Mark L. Wildhaber
  • Aaron J. DeLonay
  • Diana M. Papoulias


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. Copyright (c) Journal compilation 2009 Royal Statistical Society.

Suggested Citation

  • Scott H. Holan & Ginger M. Davis & Mark L. Wildhaber & Aaron J. DeLonay & Diana M. Papoulias, 2009. "Hierarchical Bayesian Markov switching models with application to predicting spawning success of shovelnose sturgeon," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 58(1), pages 47-64.
  • Handle: RePEc:bla:jorssc:v:58:y:2009:i:1:p:47-64

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    References listed on IDEAS

    1. Smith, Daniel R, 2002. "Markov-Switching and Stochastic Volatility Diffusion Models of Short-Term Interest Rates," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(2), pages 183-197, April.
    2. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika van der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639.
    3. Pagan, Adrian, 1984. "Econometric Issues in the Analysis of Regressions with Generated Regressors," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 25(1), pages 221-247, February.
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    Cited by:

    1. Xiong, Yingge & Tobias, Justin L. & Mannering, Fred L., 2014. "The analysis of vehicle crash injury-severity data: A Markov switching approach with road-segment heterogeneity," Transportation Research Part B: Methodological, Elsevier, vol. 67(C), pages 109-128.

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