Publication
Title
Analyzing data with robust multivariate methods and diagnostic plots
Author
Abstract
Principal Component Analysis, Canonical Correlation Analysis and Factor Analysis (Johnson and Wichern 1998) are three different methods for analyzing multivariate data. Recently robust versions of these methods have been proposed by Croux and Haesbroeck (2000), Croux and behon (2001) and Pison et al. (2002) which are able to resist the effect of outliers. Influence functions for these methods are also present. However, there does not yet exist a graphical tool to display the results of the robust data analysis in a fast way. Therefore we now construct such a diagnostic tool based on empirical influence functions. These graphics will not only allow us to detect the influential points for the multivariate statistical method but also classify the observations according to their robust distances. In this way we can identify regular points, good- (non-outlying) influential points, influential outliers, and non-influential outliers. We can down weigh the influential outliers in the classical estimation method to obtain reliable and efficient estimates of the model parameters. Some generated. data examples will be given to show how these plots can be used in practice.
Language
English
Source (book)
15th Biannual Conference on Computational Statistics (COMPSTAT), August 24-28, 2002, Berlin, germany
Publication
Heidelberg : Physica, 2002
ISBN
3-7908-1517-9
Volume/pages
p. 165-170
ISI
000179942900020
UAntwerpen
Faculty/Department
Research group
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Web of Science
Record
Identification
Creation 24.02.2012
Last edited 23.04.2017
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