title: Space-time latent component modeling of geo-referenced health data.
abstract: 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.
authors.affiliation: Division of Biostatistics and Epidemiology, College of Medicine, Medical University of South Carolina, Charleston, SC, USA. lawsonab@musc.edu
authors.forename: Andrew B
authors.initials: AB
authors.lastname: Lawson
authors.forename: Hae-Ryoung
authors.initials: HR
authors.lastname: Song
authors.forename: Bo
authors.initials: B
authors.lastname: Cai
authors.forename: Md Monir
authors.initials: MM
authors.lastname: Hossain
authors.forename: Kun
authors.initials: K
authors.lastname: Huang
mesh_terms: 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