Robust Multivariate Analysis

  • David J. Olive

Table of contents

  1. Front Matter
    Pages i-xvi
  2. David J. Olive
    Pages 1-23
  3. David J. Olive
    Pages 25-46
  4. David J. Olive
    Pages 47-85
  5. David J. Olive
    Pages 87-137
  6. David J. Olive
    Pages 139-188
  7. David J. Olive
    Pages 189-217
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    Pages 219-231
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    Pages 233-272
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    Pages 273-289
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    Pages 291-310
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    Pages 311-326
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    Pages 327-384
  14. David J. Olive
    Pages 385-391
  15. David J. Olive
    Pages 393-459
  16. David J. Olive
    Pages 461-477
  17. Back Matter
    Pages 479-501

About this book


This text presents methods that are robust to the assumption of a multivariate normal distribution or methods that are robust to certain types of outliers. Instead of using exact theory based on the multivariate normal distribution, the simpler and more applicable large sample theory is given.  The text develops among the first practical robust regression and robust multivariate location and dispersion estimators backed by theory.  

The robust techniques  are illustrated for methods such as principal component analysis, canonical correlation analysis, and factor analysis.  A simple way to bootstrap confidence regions is also provided.

Much of the research on robust multivariate analysis in this book is being published for the first time. The text is suitable for a first course in Multivariate Statistical Analysis or a first course in Robust Statistics. This graduate text is also useful for people who are familiar with the traditional multivariate topics, but want to know more about handling data sets with outliers. Many R programs and R data sets are available on the author’s website.  


Robust Statistics Prediction Region Bootstrap Confidence Region Multivariate Regression Principal Component Analysis Canonical Correlation Analysis Discriminant Analysis MANOVA

Authors and affiliations

  • David J. Olive
    • 1
  1. 1.Department of MathematicsSouthern Illinois UniversityCarbondaleUSA

Bibliographic information