Space-time latent component modeling of geo-referenced health data.
Latent structure models have been proposed in many applications. For space-time health data it is often important to be able to find the underlying trends in time, which are supported by subsets of small areas. Latent structure modeling is one such approach to this analysis. This paper presents a mixture-based approach that can be applied to component selection. The analysis of a Georgia ambulatory asthma county-level data set is presented and a simulation-based evaluation is made.
Lawson
Andrew B
AB
Division of Biostatistics and Epidemiology, College of Medicine, Medical University of South Carolina, Charleston, SC, USA. lawsonab@musc.edu
Song
Hae-Ryoung
HR
Cai
Bo
B
Hossain
Md Monir
MM
Huang
Kun
K
D000554:Ambulatory Care Facilities / Q000706:statistics & numerical data*; D001249:Asthma / Q000453:epidemiology* / Q000628:therapy; D001499:Bayes Theorem; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D005845:Georgia; D006801:Humans; D008954:Models, Biological; D012307:Risk Factors; D013029:Space-Time Clustering