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Space–time latent component modeling of geo‐referenced health data
Author(s) -
Lawson Andrew B.,
Song HaeRyoung,
Cai Bo,
Hossain Md Monir,
Huang Kun
Publication year - 2010
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.3917
Subject(s) - component (thermodynamics) , computer science , data set , data mining , set (abstract data type) , mixture model , artificial intelligence , physics , thermodynamics , programming language
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. Copyright © 2010 John Wiley & Sons, Ltd.