Nonparametric Bayesian Modeling for Density Estimation of Sea Turtle Nesting Locations Along Juno Beach in Florida

• Activity Number: 676
• Type: Topic Contributed
• Date/Time: Thursday, August 8, 2013 : 10:30 AM to 12:20 PM
• Sponsor: Section on Bayesian Statistical Science
• Author(s): Ming Wang*+ and Lance A Waller and Jian Kang
• Companies: Emory and Emory University and Emory University
• Keywords: spatial-temporal point process ; density estimation ; Dirichlet processes ; Non-parametric Bayeisan modeling ; Gibbs sampling ; hierarchical strutcure

Abstract

Spatial-temporal data are increasingly available, including one-dimensional, directionless observations observed along a line. Our motivating example involves sea turtle nesting data with space and time-specific emergence locations along Juno Beach, Palm Beach County, Florida for the years 1998-2000. To assess temporally evolving local variations in nesting density, we develop a a novel hierarchical Bayesian non-parametric model based on Dirichlet processes. Autoregressive temporal dependencies are incorporated in a three-level hierarchical structure. This model allows the potential for time-evolving mixed components/weights across groups. We compare our model with the existing models, e.g., Dirichlet process mixture models, hierarchical Dirichlet process models, and dynamic hierarchical Dirichlet process models, to show its advantage via simulation and real data application to our motivating example.