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Appendix E: Other Statistical Methods

  • Florian FrommletEmail author
  • Małgorzata Bogdan
  • David Ramsey
Chapter
Part of the Computational Biology book series (COBO, volume 18)

Abstract

Principal component analysis (PCA) is a popular technique for reducing the dimensionality of data in multivariate analysis.

References

  1. 1.
    Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via EM algorithm. J. Roy. Stat. Soc. Ser. B 39, 1–38 (1977)MathSciNetzbMATHGoogle Scholar
  2. 2.
    Koch, I.: Analysis of Multivariate and High-dimensional Data. Cambridge University Press, Cambridge Series in Statistical and Probabilistic Mathematics (2013)Google Scholar
  3. 3.
    Price, A.L., et al.: Principal components analysis corrects for stratification in genome-wide association studies. Nat. Genet. 38: 904–909 (2006)Google Scholar
  4. 4.
    Wu, C.F.J.: On the Convergence Properties of the EM Algorithm. Ann. Stat. 11: 95–103 (1983)Google Scholar

Copyright information

© Springer-Verlag London 2016

Authors and Affiliations

  • Florian Frommlet
    • 1
    Email author
  • Małgorzata Bogdan
    • 2
  • David Ramsey
    • 3
  1. 1.Center for Medical Statistics, Informatics, and Intelligent Systems Section for Medical StatisticsMedical University of ViennaViennaAustria
  2. 2.Institute of MathematicsUniversity of WrocławWrocławPoland
  3. 3.Department of Operations ResearchWrocław University of TechnologyWrocławPoland

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