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Adjusting background noise in cluster analyses of longitudinal data

Author

Listed:
  • Han, Shengtong
  • Zhang, Hongmei
  • Karmaus, Wilfried
  • Roberts, Graham
  • Arshad, Hasan

Abstract

Background noise in cluster analyses can potentially mask the true underlying patterns. To tease out patterns uniquely to certain populations, a Bayesian semi-parametric clustering method is presented. It infers and adjusts background noise. The method is built upon a mixture of the Dirichlet process and a point mass function. Simulations demonstrate the effectiveness of the proposed method. The method is then applied to analyze a longitudinal data set on allergic sensitization and asthma status.

Suggested Citation

  • Han, Shengtong & Zhang, Hongmei & Karmaus, Wilfried & Roberts, Graham & Arshad, Hasan, 2017. "Adjusting background noise in cluster analyses of longitudinal data," Computational Statistics & Data Analysis, Elsevier, vol. 109(C), pages 93-104.
    • Handle: RePEc:eee:csdana:v:109:y:2017:i:c:p:93-104
    DOI: 10.1016/j.csda.2016.11.009
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    References listed on IDEAS

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