Department of Statistics
Title: Hierarchical Bayesian Markov Switching Models with Application to Predicting Spawning Success of Shovelnose Sturgeon
Abstract:
University of Missouri, Columbia
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 spawning success of the shovelnose sturgeon, which uses both biological and behavioral (longitudinal) data.
In particular, we use data produced from a tracking study conducted in the Lower Missouri River. The data produced from this study consist of
biological variables associated with readiness to spawn along with longitudinal behavioral data collected using telemetry and archival data storage
tags. These high frequency data are complex both biologically and in the underlying behavioral 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 GARCH dynamics. Finally, in order to minimize the computational burden associated with estimation of this model, a parallel computing
approach is proposed.
This is joint work with:
Ginger M. Davis, University of Virginia
Mark L. Wildhaber, Aaron J. DeLonay, Diana M. Papoulias and Janice Bryan, USGS