國立高雄大學圖資館 |

Language: English

Back

Robust multivariate analysis

Olive, David J.

Robust multivariate analysis

Record Type:	Electronic resources : Monograph/item
Title/Author:	Robust multivariate analysisby David J. Olive.
Author:	Olive, David J.
Published:	Cham :Springer International Publishing :2017.
Description:	xvi, 501 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Multivariate analysis.
Online resource:	http://dx.doi.org/10.1007/978-3-319-68253-2
ISBN:	9783319682532$q(electronic bk.)

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$q(electronic bk.)

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

181905
Multivariate analysis.

LC Class. No.: QA278

Dewey Class. No.: 519.535

Robust multivariate analysis
LDR:02402nmm a2200325 a 4500 001 525315
003 DE-He213
005 20180522162113.0
006 m d
007 cr nn 008maaau
008 180904s2017 gw s 0 eng d
020 $a 9783319682532$q(electronic bk.)
020 $a 9783319682518$q(paper)
024 7 $a 10.1007/978-3-319-68253-2 $2 doi
035 $a 978-3-319-68253-2
040 $a GP $c GP
041 0 $a eng
050 4 $a QA278
072 7 $a PBT $2 bicssc
072 7 $a PBWL $2 bicssc
072 7 $a MAT029000 $2 bisacsh
082 0 4 $a 519.535 $2 23
090 $a QA278 $b .O48 2017
100 1 $a Olive, David J. $3 681715
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 181905
650 1 4 $a Mathematics. $3 184409
650 2 4 $a Probability Theory and Stochastic Processes. $3 274061
650 2 4 $a Statistical Theory and Methods. $3 274054
710 2 $a SpringerLink (Online service) $3 273601
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)