Chapter 13. Multivariate Outlier Detection

  1. Clemens Reimann M.Sc. in Mineralogy and Petrology, Ph.D. in Geosciences, D.Sc. in Applied Geochemistry lecturer senior geochemist director professor chairman acting vice president associate editor1,
  2. Peter Filzmoser Applied Mathematics visiting professor2,
  3. Robert G. Garrett Mining Geology and Applied Geochemistry Emeritus Scientist3,
  4. Rudolf Dutter M.Sc., Ph.D. senior statistician full professor post-doctoral fellow2

Published Online: 18 APR 2008

DOI: 10.1002/9780470987605.ch13

Book Title

Statistical Data Analysis Explained: Applied Environmental Statistics with R

Statistical Data Analysis Explained: Applied Environmental Statistics with R

Additional Information

How to Cite

Reimann, C., Filzmoser, P., Garrett, R. G. and Dutter, R. (2008) Multivariate Outlier Detection, in Statistical Data Analysis Explained: Applied Environmental Statistics with R, John Wiley & Sons, Ltd, Chichester, UK. doi: 10.1002/9780470987605.ch13

Author Information

  1. 1

    Geological Survey of Norway, Norway

  2. 2

    Vienna University of Technology, Austria

  3. 3

    Geological Survey of Canada, Canada

Publication History

  1. Published Online: 18 APR 2008
  2. Published Print: 4 APR 2008

ISBN Information

Print ISBN: 9780470985816

Online ISBN: 9780470987605

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CHAPTER TOOLS

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Keywords:

  • multivariate outlier detection;
  • Pearson correlation matrix;
  • bivariate data distribution;
  • univariate versus multivariate outlier detection;
  • Minimum Covariance Determinant (MCD);
  • robust Mahalanobis distances;
  • automated multivariate outlier detection and visualisation;
  • Mahalanobis distance computation

Summary

This chapter contains sections titled:

  • Univariate versus multivariate outlier detection

  • Robust versus non-robust outlier detection

  • The chi-square plot

  • Automated multivariate outlier detection and visualisation

  • Other graphical approaches for identifying outliers and groups

  • Summary

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