Online Program Home
  My Program

Abstract Details

Activity Number: 217 - Recent Developments in Spatial Statistics
Type: Topic Contributed
Date/Time: Monday, July 31, 2017 : 2:00 PM to 3:50 PM
Sponsor: SSC
Abstract #324685 View Presentation
Title: A Spatio-Temporal Cluster Process for Modeling Storm Cells
Author(s): Charmaine Dean and Alisha Albert-Green*
Companies: University of Waterloo and University of Toronto
Keywords: Point Processes ; Log-Gaussian Cox Process ; Storm Cells ; Clustering ; Spatial Methods
Abstract:

Storm cells are the smallest component of a storm-producing system. A cluster of such cells is referred to as a storm and a storm system consists of a cluster of storms. This research develops a model for these storm cells over space and time. Specifically, we extend the Neyman-Scott process, which is commonly employed for the analysis of clustered point processes, to account for the hierarchical clustering present in our data. We do this by allowing the parents to follow a doubly stochastic process, namely a log-Gaussian Cox process. This model is applied to storm cell data from the Bismarck radar station in North Dakota, USA and parameter estimation is done using minimum contrast estimation.


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

Back to the full JSM 2017 program

 
 
Copyright © American Statistical Association