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A mixture model approach for the analysis of microarray gene expression data

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Listed:
  • Allison, David B.
  • Gadbury, Gary L.
  • Heo, Moonseong
  • Fernandez, Jose R.
  • Lee, Cheol-Koo
  • Prolla, Tomas A.
  • Weindruch, Richard

Abstract

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Suggested Citation

  • Allison, David B. & Gadbury, Gary L. & Heo, Moonseong & Fernandez, Jose R. & Lee, Cheol-Koo & Prolla, Tomas A. & Weindruch, Richard, 2002. "A mixture model approach for the analysis of microarray gene expression data," Computational Statistics & Data Analysis, Elsevier, vol. 39(1), pages 1-20, March.
    • Handle: RePEc:eee:csdana:v:39:y:2002:i:1:p:1-20
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    References listed on IDEAS

    as
    1. Samaniego, Francisco J. & Vestrup, Eric, 1999. "On improving standard estimators via linear empirical Bayes methods," Statistics & Probability Letters, Elsevier, vol. 44(3), pages 309-318, September.
    2. B. Devlin & Kathryn Roeder, 1999. "Genomic Control for Association Studies," Biometrics, The International Biometric Society, vol. 55(4), pages 997-1004, December.
    3. G. J. McLachlan, 1987. "On Bootstrapping the Likelihood Ratio Test Statistic for the Number of Components in a Normal Mixture," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 36(3), pages 318-324, November.
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