Online Program Home
My Program

Abstract Details

Activity Number: 62 - Modeling and Inference Using Stochastic Differential Equations
Type: Topic Contributed
Date/Time: Sunday, July 29, 2018 : 4:00 PM to 5:50 PM
Sponsor: Section on Statistics and the Environment
Abstract #330681
Title: Stochastic Differential Equation to Model Movement Data in Ecology
Author(s): Marie-Pierre Etienne*
Keywords: Stochastic Differential equation; Particle algorithm

Using GPS data to study the relationship between individuals within species, between individuals from different species or relationship linking animals and their environment are a major aspect of movement ecology. When focusing on the relation between individuals and their environment, the movement might be thought as a solution to a stochastic differential equation whose drift is the gradient of a multimodal potential surface, describing the atractivity of the environment. This offers a new flexible approach among the popular potential based movement models in ecology. To perform parameter inference, the widely used Euler method is compared with two other pseudo-likelihood procedures and with a Monte Carlo Expectation Maximization approach based on exact simulation of diffusions. Performances of all methods are assessed with simulated data and with a data set of fishing vessels trajectories. We prove that the usual Euler method performs worse than the other procedures for all sampling schemes

Authors who are presenting talks have a * after their name.

Back to the full JSM 2018 program