A Bayesian Nonparametric Method for Spatial Point Processes with Application to Sea Turtles' Nesting Patterns

• Activity Number: 92
• Type: Invited
• Date/Time: Sunday, August 4, 2013 : 8:30 PM to 10:30 PM
• Sponsor: Section on Statistics and the Environment
• Author(s): Gavino Puggioni*+ and Lance A Waller
• Companies: University of Rhode Island and Emory University
• Keywords: Dirichlet Process ; Point Process ; Sea Turtles ; Spatio temporal process ; Nonparametric ; Dynamic Models

Abstract

We propose a nonparametric method to estimate the intensity of a point process observed in space and time. The modeling procedure, treated as a dynamic density estimation problem, involves the specification of a prior based on a Dirichlet Process mixture of Normal distributions at each point in time. Temporal dependence is introduced through the atoms that evolve as Dynamic Linear Models. The methodology is complemented by an application to sea turtle nesting patterns observed at Juno Beach, FL from 1999 to 2001.