A new robust diagnostic plot for classifying good and bad high leverage points in a multiple linear regression model
Identification of high leverage point is crucial because it is responsible for inaccurate prediction and invalid inferential statement as it has a larger impact on the computed values of various estimates. It is essential to classify the high leverage points into good and bad leverage points because...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi Publishing Corporation
2015
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| Online Access: | http://psasir.upm.edu.my/id/eprint/43528/ http://psasir.upm.edu.my/id/eprint/43528/1/A%20New%20Robust%20Diagnostic%20Plot%20for%20Classifying%20Good%20and%20Bad.pdf |
| _version_ | 1848850252691406848 |
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| author | Alguraibawi, Mohammed Midi, Habshah Rahmatullah Imon, A. H. M. |
| author_facet | Alguraibawi, Mohammed Midi, Habshah Rahmatullah Imon, A. H. M. |
| author_sort | Alguraibawi, Mohammed |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Identification of high leverage point is crucial because it is responsible for inaccurate prediction and invalid inferential statement as it has a larger impact on the computed values of various estimates. It is essential to classify the high leverage points into good and bad leverage points because only the bad leverage points have an undue effect on the parameter estimates. It is now evident that when a group of high leverage points is present in a data set, the existing robust diagnostic plot fails to classify them correctly. This problem is due to the masking and swamping effects. In this paper, we propose a new robust diagnostic plot to correctly classify the good and bad leverage points by reducing both masking and swamping effects. The formulation of the proposed plot is based on the Modified Generalized Studentized Residuals. We investigate the performance of our proposed method by employing a Monte Carlo simulation study and some well-known data sets. The results indicate that the proposed method is able to improve the rate of detection of bad leverage points and also to reduce swamping and masking effects. |
| first_indexed | 2025-11-15T10:03:20Z |
| format | Article |
| id | upm-43528 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T10:03:20Z |
| publishDate | 2015 |
| publisher | Hindawi Publishing Corporation |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-435282016-06-29T01:10:35Z http://psasir.upm.edu.my/id/eprint/43528/ A new robust diagnostic plot for classifying good and bad high leverage points in a multiple linear regression model Alguraibawi, Mohammed Midi, Habshah Rahmatullah Imon, A. H. M. Identification of high leverage point is crucial because it is responsible for inaccurate prediction and invalid inferential statement as it has a larger impact on the computed values of various estimates. It is essential to classify the high leverage points into good and bad leverage points because only the bad leverage points have an undue effect on the parameter estimates. It is now evident that when a group of high leverage points is present in a data set, the existing robust diagnostic plot fails to classify them correctly. This problem is due to the masking and swamping effects. In this paper, we propose a new robust diagnostic plot to correctly classify the good and bad leverage points by reducing both masking and swamping effects. The formulation of the proposed plot is based on the Modified Generalized Studentized Residuals. We investigate the performance of our proposed method by employing a Monte Carlo simulation study and some well-known data sets. The results indicate that the proposed method is able to improve the rate of detection of bad leverage points and also to reduce swamping and masking effects. Hindawi Publishing Corporation 2015 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/43528/1/A%20New%20Robust%20Diagnostic%20Plot%20for%20Classifying%20Good%20and%20Bad.pdf Alguraibawi, Mohammed and Midi, Habshah and Rahmatullah Imon, A. H. M. (2015) A new robust diagnostic plot for classifying good and bad high leverage points in a multiple linear regression model. Mathematical Problems in Engineering, 2015. pp. 1-12. ISSN 1024-123X http://www.hindawi.com/ 10.1155/2015/279472 |
| spellingShingle | Alguraibawi, Mohammed Midi, Habshah Rahmatullah Imon, A. H. M. A new robust diagnostic plot for classifying good and bad high leverage points in a multiple linear regression model |
| title | A new robust diagnostic plot for classifying good and bad high leverage points in a multiple linear regression model |
| title_full | A new robust diagnostic plot for classifying good and bad high leverage points in a multiple linear regression model |
| title_fullStr | A new robust diagnostic plot for classifying good and bad high leverage points in a multiple linear regression model |
| title_full_unstemmed | A new robust diagnostic plot for classifying good and bad high leverage points in a multiple linear regression model |
| title_short | A new robust diagnostic plot for classifying good and bad high leverage points in a multiple linear regression model |
| title_sort | new robust diagnostic plot for classifying good and bad high leverage points in a multiple linear regression model |
| url | http://psasir.upm.edu.my/id/eprint/43528/ http://psasir.upm.edu.my/id/eprint/43528/ http://psasir.upm.edu.my/id/eprint/43528/ http://psasir.upm.edu.my/id/eprint/43528/1/A%20New%20Robust%20Diagnostic%20Plot%20for%20Classifying%20Good%20and%20Bad.pdf |