Online Program Home
My Program

Abstract Details

Activity Number: 368
Type: Contributed
Date/Time: Tuesday, August 2, 2016 : 10:30 AM to 12:20 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #318619 View Presentation
Title: Spatial Mixture Multiscale Modeling for Aggregated Health Data
Author(s): Mehreteab Aregay* and Andrew B. Lawson
Companies: Medical University of South Carolina and Medical University of South Carolina
Keywords: correlated heterogeneity ; multiscale models ; scaling effect ; spatial mixture model ; uncorrelated heterogeneity

One of the main goals in spatial epidemiology is to study the geographical pattern of disease risks. For such purpose, the convolution model composed of correlated and uncorrelated components is often used. However, one of the two components could be predominant in some regions. To investigate the predominance of the correlated or uncorrelated component for multiple scale data, we propose four different spatial mixture multiscale models by using mixing spatially-varying probability weights of correlated and uncorrelated heterogeneity. The first model assumes that there is no linkage between the different scales. The second model introduces linkage between finer and coarser scales via a shared uncorrelated component of the mixture convolution model. The third model is similar to the second model but the linkage between the scales is introduced through the correlated component. Finally, the fourth model accommodates for a scale effect by sharing both the correlated and uncorrelated heterogeneity simultaneously. We applied these models to real and simulated data, and we found that the fourth model is the best model followed by the second model.

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

Back to the full JSM 2016 program

Copyright © American Statistical Association