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...
| Published in: | CHUSER 2012 - 2012 IEEE Colloquium on Humanities, Science and Engineering Research |
|---|---|
| Main Author: | |
| Format: | Conference paper |
| Language: | English |
| Published: |
2012
|
| Online Access: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-84877673746&doi=10.1109%2fCHUSER.2012.6504365&partnerID=40&md5=c932bb88749a2294b55d01be22ca4f2b |
| id |
2-s2.0-84877673746 |
|---|---|
| spelling |
2-s2.0-84877673746 Ahmad S.; Ramli N.M.; Midi H. Outlier detection in logistic regression and its application in medical data analysis 2012 CHUSER 2012 - 2012 IEEE Colloquium on Humanities, Science and Engineering Research 10.1109/CHUSER.2012.6504365 https://www.scopus.com/inward/record.uri?eid=2-s2.0-84877673746&doi=10.1109%2fCHUSER.2012.6504365&partnerID=40&md5=c932bb88749a2294b55d01be22ca4f2b 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. © 2012 IEEE. English Conference paper All Open Access; Green Open Access |
| author |
Ahmad S.; Ramli N.M.; Midi H. |
| spellingShingle |
Ahmad S.; Ramli N.M.; Midi H. Outlier detection in logistic regression and its application in medical data analysis |
| author_facet |
Ahmad S.; Ramli N.M.; Midi H. |
| author_sort |
Ahmad S.; Ramli N.M.; Midi H. |
| title |
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_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_sort |
Outlier detection in logistic regression and its application in medical data analysis |
| publishDate |
2012 |
| container_title |
CHUSER 2012 - 2012 IEEE Colloquium on Humanities, Science and Engineering Research |
| container_volume |
|
| container_issue |
|
| doi_str_mv |
10.1109/CHUSER.2012.6504365 |
| url |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-84877673746&doi=10.1109%2fCHUSER.2012.6504365&partnerID=40&md5=c932bb88749a2294b55d01be22ca4f2b |
| 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. © 2012 IEEE. |
| publisher |
|
| issn |
|
| language |
English |
| format |
Conference paper |
| accesstype |
All Open Access; Green Open Access |
| record_format |
scopus |
| collection |
Scopus |
| _version_ |
1809677913231982592 |