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© 2017

Robust Multivariate Analysis

Benefits

  • Includes dozens of R functions for making plots and estimators

  • Problems included at the end of every chapter

  • Code available for download on the author's website

Textbook

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
  8. David J. Olive
    Pages 219-231
  9. David J. Olive
    Pages 233-272
  10. David J. Olive
    Pages 273-289
  11. David J. Olive
    Pages 291-310
  12. David J. Olive
    Pages 311-326
  13. David J. Olive
    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

Introduction

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.  

Keywords

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

Authors and affiliations

  1. 1.Department of MathematicsSouthern Illinois UniversityCarbondaleUSA

About the authors

David Olive is a Professor at Southern Illinois University, Carbondale, IL, USA.  His research interests include the development of computationally practical robust multivariate location and dispersion estimators, robust multiple linear regression estimators, and resistant dimension reduction estimators. 

Bibliographic information

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Reviews

“This monograph provides a comprehensive introduction to the mathematical theory of framelets and discrete framelet transforms. … This monograph is well-written for a broad readership and very convenient as a textbook for graduate students and as an advanced reference guide for researchers in applied mathematics, physics, and engineering. Doubtless, this work will stimulate further research on framelets.” (Manfred Tasche, zbMATH 1387.42001, 2018)