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Space-Time Areal Mixture Model: Relabeling Algorithm and Model Selection Issues.

M M Hossain1, A B Lawson2, B Cai3

  • 1Division of Biostatistics and Epidemiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.

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|September 16, 2014
PubMed
Summary
This summary is machine-generated.

Model selection criteria in spatial mixture models may not align with optimal relabeling algorithms for cluster analysis. This can lead to suboptimal cluster identification when using Bayesian mixture models.

Keywords:
DICSpace-time mixture modelhomogeneous covariate effectloss functionrelabeling algorithm

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Area of Science:

  • Statistics
  • Computational Statistics
  • Data Mining

Background:

  • Spatial mixture models are increasingly popular for cluster analysis.
  • Model selection criteria aid in choosing parsimonious models.
  • Bayesian implementations of mixture models often face the label-switching problem.

Purpose of the Study:

  • To investigate the relationship between model selection criteria and relabeling algorithms in spatial mixture models.
  • To highlight potential discrepancies between statistically selected best-fit models and optimally relabeled models for cluster identification.

Main Methods:

  • Utilized space-time mixture of Poisson regression models with homogeneous covariate effects.
  • Applied model selection criteria to assess model fit.
  • Employed relabeling algorithms to address the label-switching problem in Bayesian mixture models.

Main Results:

  • The model selected by model selection criteria did not consistently align with the model identified by the optimal relabeling algorithm.
  • Discrepancies were observed in both real and simulated datasets.
  • The best-fit model according to selection criteria may not yield the optimal relabeling for cluster identification.

Conclusions:

  • Readers should be cautious when using model selection criteria and relabeling algorithms in tandem for cluster analysis.
  • Applying a relabeling algorithm to the best-fit model may not guarantee the optimal cluster solution.
  • Awareness of this potential issue is crucial for accurate cluster identification using Bayesian mixture models.