Publication
Title
Diagnostic plots for robust multivariate methods
Author
Abstract
Robust techniques for multivariate statistical methods-such as principal component analysis, canonical correlation analysis, and factor analysis-have been recently constructed. In contrast to the classical approach, these robust techniques are able to resist the effect of outliers. However, there does not yet exist a graphical tool to identify in a comprehensive way the data points that do not obey the model assumptions. Our goal is to construct such graphics based on empirical influence functions. These graphics not only detect the influential points but also classify the observations according to their robust distances. In this way the observations are divided into four different classes which are regular points, nonoutlying influential points, influential outliers, and noninfluential outliers. We thus gain additional insight in the data by detecting different types of deviating observations. Some real data examples will be given to show how these plots can be used in practice.
Language
English
Source (journal)
Journal of computational and graphical statistics. - Alexandria, Va
Publication
Alexandria, Va : 2004
ISSN
1061-8600
Volume/pages
13:2(2004), p. 310-329
ISI
000221768700003
Full text (Publisher's DOI)
Full text (publisher's version - intranet only)
UAntwerpen
Faculty/Department
Research group
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Web of Science
Record
Identification
Creation 03.01.2013
Last edited 09.07.2017
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