Robust model selection and outlier detection in linear regressions

In this thesis, we study the problems of robust model selection and outlier detection in linear regression. The results of data analysis based on linear regressions are highly sensitive to model choice and the existence of outliers in the data. This thesis aims to help researchers to choose the correct model when their data could be contaminated with outliers, to detect possible outliers in their data, and to study the impact that such outliers have on their analysis. First, we discuss the problem of robust model selection. Many methods for performing model selection were designed with the standard error model ... and least squares estimation in mind. These methods often perform poorly on real world data, which can include outliers. Robust model selection methods aim to protect us from outliers and capture the model that represents the bulk of the data. We review the currently available model selection algorithms (both non-robust and robust) and present five new algorithms. Our algorithms aim to improve upon the currently available algorithms, both in terms of accuracy and computational feasibility. We demonstrate the improved accuracy of our algorithms via a simulation study and a study on a real world data set.

Department dc:contributor.department

Massachusetts Institute of Technology. Operations Research Center.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2006

Author dc:creator

• McCann, Lauren, Ph. D. Massachusetts Institute of Technology

Advisor dc:contributor.advisor

• Roy E. Welsch.

• Operations Research Center.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/36222

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/36222