Online Program Home
My Program

Abstract Details

Activity Number: 317 - The Future of Spatial and Spatio-Temporal Statistics: Perspectives for the Next Generation of Leaders
Type: Invited
Date/Time: Tuesday, July 31, 2018 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics and the Environment
Abstract #326597 Presentation
Title: Horse for Courses: Empirical Vs Mechanistic Modeling for Spatio-Temporal Point Process Data
Author(s): Peter John Diggle*
Companies: Lancaster University
Keywords: health survreillance; infectious disease models; spatial statistics; point processes

The foremost challenge for statistical science in the twenty-first century is to sustain and grow the place of statistical method (singular) as an integral component of scientific method. I shall offer a personal response to this challenge in the specific setting of spatio-temporal point process data.

Generic methods of analysis for spatio-temporal point process data are now well developed and accessible as R packages. Using examples from the biomedical and health sciences, I will argue that as the twenty-first century progresses, effective strategies for modelling and inference will increasingly depend on close collaboration between statisticians and subject-matter experts.

Risking over-simplification, I will make a distinction between: empirical models, which predominate within the data science community and are well-suited to problems where the goal is prediction, the data are abundant and the underlying scientific process is poorly understood; and mechanistic models, which are indicated when the goal is estimation, the data are sparse and the underlying scientific process is well understood.

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

Back to the full JSM 2018 program