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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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    1. Li-Xuan Qin & Steven G. Self, 2006. "The Clustering of Regression Models Method with Applications in Gene Expression Data," Biometrics, The International Biometric Society, vol. 62(2), pages 526-533, June.
    2. Dunson, David B. & Herring, Amy H. & Engel, Stephanie M., 2008. "Bayesian Selection and Clustering of Polymorphisms in Functionally Related Genes," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 534-546, June.
    3. Bigelow, Jamie L. & Dunson, David B., 2009. "Bayesian Semiparametric Joint Models for Functional Predictors," Journal of the American Statistical Association, American Statistical Association, vol. 104(485), pages 26-36.
    4. Zhang, Hongmei & Ghosh, Kaushik & Ghosh, Pulak, 2012. "Sampling designs via a multivariate hypergeometric-Dirichlet process model for a multi-species assemblage with unknown heterogeneity," Computational Statistics & Data Analysis, Elsevier, vol. 56(8), pages 2562-2573.
    5. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Linde, 2014. "The deviance information criterion: 12 years on," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 76(3), pages 485-493, June.
    6. Efron B. & Tibshirani R. & Storey J.D. & Tusher V., 2001. "Empirical Bayes Analysis of a Microarray Experiment," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1151-1160, December.
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