Space-time Areal Mixture Model: Relabeling Algorithm and Model Selection Issues
Document Type
Article
Publication Date
2014
Keywords
space-time mixture model, homogeneous covariate effect, relabeling algorithm, loss function, DIC
Digital Object Identifier (DOI)
https://doi.org/10.1002/env.2265
Abstract
With the growing popularity of spatial mixture models in cluster analysis, model selection criteria have become an established tool in the search for parsimony. However, the label-switching problem is often inherent in Bayesian implementation of mixture models, and a variety of relabeling algorithms have been proposed. We use a space-time mixture of Poisson regression models with homogeneous covariate effects to illustrate that the best model selected by using model selection criteria does not always support the model that is chosen by the optimal relabeling algorithm. The results are illustrated for real and simulated datasets. The objective is to make the reader aware that if the purpose of statistical modeling is to identify clusters, applying a relabeling algorithm to the model with the best fit may not generate the optimal relabeling. Copyright © 2014 John Wiley & Sons, Ltd.
Was this content written or created while at USF?
Yes
Citation / Publisher Attribution
Environmetrics, v. 25, issue 2, p. 84-96
Scholar Commons Citation
Hossain, M. M.; Lawson, A. B.; Cai, B.; Choi, J.; Liu, J.; and Kirby, Russell S., "Space-time Areal Mixture Model: Relabeling Algorithm and Model Selection Issues" (2014). Community and Family Health Faculty Publications. 87.
https://digitalcommons.usf.edu/cfh_facpub/87
