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Robust multivariate analysis

Olive, David J.

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Robust multivariate analysis

Record Type:	Electronic resources : Monograph/item
Title/Author:	Robust multivariate analysis/ by David J. Olive.
Author:	Olive, David J.
Published:	Cham :Springer International Publishing : : 2017.,
Description:	xvi, 501 p. :ill., digital ;24 cm.
[NT 15003449]:	Introduction -- Multivariate Distributions -- Elliptically Contoured Distributions -- MLD Estimators -- DD Plots and Prediction Regions -- Principal Component Analysis -- Canonical Correlation Analysis -- Discrimination Analysis -- Hotelling's T^2 Test -- MANOVA -- Factor Analysis -- Multivariate Linear Regression -- Clustering -- Other Techniques -- Stuff for Students.
Contained By:	Springer eBooks
Subject:	Multivariate analysis. -
Online resource:	http://dx.doi.org/10.1007/978-3-319-68253-2
ISBN:	9783319682532

Robust multivariate analysis
Olive, David J.

Robust multivariate analysis[electronic resource] /by David J. Olive. - Cham :Springer International Publishing :2017. - xvi, 501 p. :ill., digital ;24 cm.

Introduction -- Multivariate Distributions -- Elliptically Contoured Distributions -- MLD Estimators -- DD Plots and Prediction Regions -- Principal Component Analysis -- Canonical Correlation Analysis -- Discrimination Analysis -- Hotelling's T^2 Test -- MANOVA -- Factor Analysis -- Multivariate Linear Regression -- Clustering -- Other Techniques -- Stuff for Students.

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.

ISBN: 9783319682532

Standard No.: 10.1007/978-3-319-68253-2doiSubjects--Topical Terms:

517467
Multivariate analysis.

LC Class. No.: QA278

Dewey Class. No.: 519.535

Robust multivariate analysis
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100 1 $a Olive, David J. $3 2068591
245 1 0 $a Robust multivariate analysis $h [electronic resource] / $c by David J. Olive.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2017.
300 $a xvi, 501 p. : $b ill., digital ; $c 24 cm.
505 0 $a Introduction -- Multivariate Distributions -- Elliptically Contoured Distributions -- MLD Estimators -- DD Plots and Prediction Regions -- Principal Component Analysis -- Canonical Correlation Analysis -- Discrimination Analysis -- Hotelling's T^2 Test -- MANOVA -- Factor Analysis -- Multivariate Linear Regression -- Clustering -- Other Techniques -- Stuff for Students.
520 $a 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.
650 0 $a Multivariate analysis. $3 517467
650 1 4 $a Mathematics. $3 515831
650 2 4 $a Probability Theory and Stochastic Processes. $3 891080
650 2 4 $a Statistical Theory and Methods. $3 891074
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-68253-2
950 $a Mathematics and Statistics (Springer-11649)