Online Program Home
My Program

Abstract Details

Activity Number: 359
Type: Contributed
Date/Time: Tuesday, August 2, 2016 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics and the Environment
Abstract #319538 View Presentation
Title: Cluster Detection of Spatial Regression Coefficients
Author(s): Junho Lee* and Ronald Gangnon and Jun Zhu
Companies: University of Wisconsin and University of Wisconsin and University of Wisconsin - Madison
Keywords: disease mapping ; geographically weighted regression ; spatial cluster detection ; spatial regression coefficient ; spatial scan statistic ; varying coefficient regression
Abstract:

Popular approaches to spatial cluster detection, such as the spatial scan statistic, are defined in terms of the responses. Here, we consider a varying-coefficient regression and spatial clusters in the regression coefficients. For varying-coefficient regression, such as the geographically weighted regression, different regression coefficients are obtained for different spatial units. It is often of interest to the practitioners to identify clusters of spatial units with distinct patterns in a regression coefficient, but there is no formal statistical methodology for that. Rather, cluster identification is often ad-hoc such as by eyeballing the map of fitted regression coefficients and discerning patterns. In this paper, we develop new methodology for spatial cluster detection in the regression setting based on hypotheses testing. We evaluate our methods in terms of power and coverages for true clusters via simulation studies. For illustration, our methodology is applied to a cancer mortality dataset.


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

Back to the full JSM 2016 program

 
 
Copyright © American Statistical Association