Home Robust Multivariate Analysis by David J. Olive (ISBN: 9783319885711)

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by David J. Olive

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From the publisher

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

From the rear cover

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.

Details

Title Robust Multivariate Analysis
Author David J. Olive
Binding Paperback
Pages 501
Volumes 1
Language ENG
Publisher Springer
ISBN 9783319885711 / 3319885715
Weight 2.33 lbs (1.06 kg)
Dimensions 9.25 x 6.1 x 1.24 in (23.50 x 15.49 x 3.15 cm)
Category Mathematics
Dewey Decimal Code 519.2

About the author

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.

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