ITEM METADATA RECORD
Title: Diagnostic plots for robust multivariate methods
Authors: Pison, G ×
Van Aelst, Stefan #
Issue Date: 2004
Publisher: AMER STATISTICAL ASSOC
Series Title: JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS vol:13 issue:2 pages:310-329
Abstract: Recently robust techniques for multivariate statistical methods such as principal
component analysis, canonical correlation analysis and factor analysis have been con-
structed. 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 in four different classes
which are regular points, non-outlying influential points, influential outliers, and non-
influential 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.
ISSN: 1061-8600
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Non-KU Leuven Association publications
× corresponding author
# (joint) last author

Files in This Item:
File Description Status SizeFormat
diagnosticPlots_report.pdf Published 288KbAdobe PDFView/Open

 


All items in Lirias are protected by copyright, with all rights reserved.

© Web of science