Outlier detection in logistic regression and its application in medical data analysis
The application of logistic regression is widely used in medical research. The detection of outliers has become an essential part of logistic regression. It is often observed outliers have a considerable influence on the analysis results, which may lead the study to the wrong conclusions. Many proce...
| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
IEEE
2012
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| Online Access: | http://psasir.upm.edu.my/id/eprint/45052/1/Outlier%20detection%20in%20logistic%20regression%20and%20its%20application%20in%20medical%20data%20analysis.pdf |
| _version_ | 1796974529969389568 |
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| author | Ahmad, Sanizah Mohamed Ramli, Norazan Midi, Habshah |
| author_facet | Ahmad, Sanizah Mohamed Ramli, Norazan Midi, Habshah |
| author_sort | Ahmad, Sanizah |
| collection | UPM |
| description | The application of logistic regression is widely used in medical research. The detection of outliers has become an essential part of logistic regression. It is often observed outliers have a considerable influence on the analysis results, which may lead the study to the wrong conclusions. Many procedures for the identification of outliers in logistic regression are available in the literature. In this paper, four methods for outlier detection have been investigated and compared through numerical examples. |
| first_indexed | 2024-03-06T08:58:23Z |
| format | Conference or Workshop Item |
| id | upm.eprints-45052 |
| institution | Universiti Putra Malaysia |
| language | English |
| last_indexed | 2024-03-06T08:58:23Z |
| publishDate | 2012 |
| publisher | IEEE |
| record_format | dspace |
| spelling | upm.eprints-450522020-08-04T02:41:39Z http://psasir.upm.edu.my/id/eprint/45052/ Outlier detection in logistic regression and its application in medical data analysis Ahmad, Sanizah Mohamed Ramli, Norazan Midi, Habshah The application of logistic regression is widely used in medical research. The detection of outliers has become an essential part of logistic regression. It is often observed outliers have a considerable influence on the analysis results, which may lead the study to the wrong conclusions. Many procedures for the identification of outliers in logistic regression are available in the literature. In this paper, four methods for outlier detection have been investigated and compared through numerical examples. IEEE 2012 Conference or Workshop Item PeerReviewed text en http://psasir.upm.edu.my/id/eprint/45052/1/Outlier%20detection%20in%20logistic%20regression%20and%20its%20application%20in%20medical%20data%20analysis.pdf Ahmad, Sanizah and Mohamed Ramli, Norazan and Midi, Habshah (2012) Outlier detection in logistic regression and its application in medical data analysis. In: 2012 IEEE Colloquium on Humanities, Science & Engineering Research (CHUSER 2012), 3-4 Dec. 2012, Kota Kinabalu, Sabah. (pp. 503-507). 10.1109/CHUSER.2012.6504365 |
| spellingShingle | Ahmad, Sanizah Mohamed Ramli, Norazan Midi, Habshah Outlier detection in logistic regression and its application in medical data analysis |
| title | Outlier detection in logistic regression and its application in medical data analysis |
| title_full | Outlier detection in logistic regression and its application in medical data analysis |
| title_fullStr | Outlier detection in logistic regression and its application in medical data analysis |
| title_full_unstemmed | Outlier detection in logistic regression and its application in medical data analysis |
| title_short | Outlier detection in logistic regression and its application in medical data analysis |
| title_sort | outlier detection in logistic regression and its application in medical data analysis |
| url | http://psasir.upm.edu.my/id/eprint/45052/1/Outlier%20detection%20in%20logistic%20regression%20and%20its%20application%20in%20medical%20data%20analysis.pdf |
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