Overview
- 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
- Includes supplementary material: sn.pub/extras
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About this book
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.
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Table of contents (15 chapters)
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Bibliographic Information
Book Title: Robust Multivariate Analysis
Authors: David J. Olive
DOI: https://doi.org/10.1007/978-3-319-68253-2
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: Springer International Publishing AG 2017
Hardcover ISBN: 978-3-319-68251-8Published: 13 December 2017
Softcover ISBN: 978-3-319-88571-1Published: 23 May 2018
eBook ISBN: 978-3-319-68253-2Published: 28 November 2017
Edition Number: 1
Number of Pages: XVI, 501
Number of Illustrations: 70 b/w illustrations, 6 illustrations in colour
Topics: Probability Theory and Stochastic Processes, Statistical Theory and Methods