Multiple outliers detection procedures in linear regression
This paper describes a procedure for identifying multiple outliers in linear regression. This procedure uses a robust fit which is the least of trimmed of squares (LTS) and the single linkage clustering method to obtain the potential outliers. Then multiple-case diagnostics are used to obtain the ou...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | en |
| Published: |
Faculty of Science
2003
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| Subjects: | |
| Online Access: | http://eprints.utm.my/1193/1/RobiahAdnan2003_MultipleOutliersDetectionProcedures.pdf http://eprints.utm.my/1193/ http://www.fs.utm.my/matematika/content/view/79/31/ |
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| Summary: | This paper describes a procedure for identifying multiple outliers in linear regression. This procedure uses a robust fit which is the least of trimmed of squares (LTS) and the single linkage clustering method to obtain the potential outliers. Then multiple-case diagnostics are used to obtain the outliers from these potential outliers. The performance of this procedure is also compared to Serbert's method. Monte Carlo simulations are used in determining which procedure performed best in all of the linear regression scenarios |
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