Identifying outliers and influential observations in general linear regression models

Identifying outliers and/or influential observations is a fundamental step in any statistical analysis, since their presence is likely to lead to erroneous results. Numerous measures have been proposed for detecting outliers and assessing the influence of observations on least squares regression results. Since outliers can arise in different ways, the above mentioned measures are based on motivational arguments and they are designed to measure the influence of observations on different aspects of various regression results. In what follows, we investigate how one can combine different test statistics based on residuals and diagnostic plots to identify outliers and influential observations (both in the single and multiple case) in general linear regression models.

Grantor dc:publisher.institution

Department of Statistical Sciences

Year dc:date.issued

2004

Author dc:creator

• Katshunga, Dominique

Advisor dc:contributor.advisor

• Troskie, Casper G

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/11427/6772

OAI identifier oai:identifier

oai:open.uct.ac.za:11427/6772