Robust methods for detecting bad leverage point in logistic regression

Ebru Gündoğan Aşık; Arzu Altin Yavuz; Zafer Küçük

Makale detayları

Robust methods for detecting bad leverage point in logistic regression

Yazarlar:

Ebru Gündoğan Aşık | Arzu Altin Yavuz | Zafer Küçük

Yayıncı:

Yildiz Technical University

Ana Başlıklar:

Biochemistry and Cell Biology (Other) | Clinical Chemistry

Dergi:

Sigma Journal of Engineering and Natural Sciences Vol.42 Issue 5, pp.1344-1356

Belge Tipi:

Makale

Yayın Tarihi:

4/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001329131

pISSN:

1304-7191

Anahtar kelimeler:

Özet:

 High-leverage points, known as good and bad leverage points, are also known as points away from center of x space. Bad leverage points are marginal values that show the incompatibility with misclassified observations and other observation values at x space. In the identification of bad leverage points, the problems of masking and swamping constitute a problem for the logistic regression model just as in the linear regression model. In this research, in addition to existing deviance components DEVC, robust deviance components RobDEVC that are used to identify bad leverage points, different robust methods recommended to be used at the management of deviance components were examined. Also, for these methods, robust cut-off value combinations were examined as well. With the conducted simulation, robust methods recommended to be used in the deviance component method have shown better performance to identify bad leverage points by showing different cut-off values.

 High-leverage points, known as good and bad leverage points, are also known as points away from center of x space. Bad leverage points are marginal values that show the incompatibility with misclassified observations and other observation values at x space. In the identification of bad leverage points, the problems of masking and swamping constitute a problem for the logistic regression model just as in the linear regression model. In this research, in addition to existing deviance components DEVC, robust deviance components RobDEVC that are used to identify bad leverage points, different robust methods recommended to be used at the management of deviance components were examined. Also, for these methods, robust cut-off value combinations were examined as well. With the conducted simulation, robust methods recommended to be used in the deviance component method have shown better performance to identify bad leverage points by showing different cut-off values.

 High-leverage points, known as good and bad leverage points, are also known as points away from center of x space. Bad leverage points are marginal values that show the incompatibility with misclassified observations and other observation values at x space. In the identification of bad leverage points, the problems of masking and swamping constitute a problem for the logistic regression model just as in the linear regression model. In this research, in addition to existing deviance components DEVC, robust deviance components RobDEVC that are used to identify bad leverage points, different robust methods recommended to be used at the management of deviance components were examined. Also, for these methods, robust cut-off value combinations were examined as well. With the conducted simulation, robust methods recommended to be used in the deviance component method have shown better performance to identify bad leverage points by showing different cut-off values.

 High-leverage points, known as good and bad leverage points, are also known as points away from center of x space. Bad leverage points are marginal values that show the incompatibility with misclassified observations and other observation values at x space. In the identification of bad leverage points, the problems of masking and swamping constitute a problem for the logistic regression model just as in the linear regression model. In this research, in addition to existing deviance components DEVC, robust deviance components RobDEVC that are used to identify bad leverage points, different robust methods recommended to be used at the management of deviance components were examined. Also, for these methods, robust cut-off value combinations were examined as well. With the conducted simulation, robust methods recommended to be used in the deviance component method have shown better performance to identify bad leverage points by showing different cut-off values.

Makale detayları

Robust methods for detecting bad leverage point in logistic regression

Yazarlar:

Ebru Gündoğan Aşık | Arzu Altin Yavuz | Zafer Küçük

Yayıncı:

Yildiz Technical University

Ana Başlıklar:

Biochemistry and Cell Biology (Other) | Clinical Chemistry

Dergi:

Sigma Journal of Engineering and Natural Sciences Vol.42 Issue 5, pp.1344-1356

Belge Tipi:

Makale

Yayın Tarihi:

4/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001329131

pISSN:

1304-7191

Bad Leverage

High-Leverage

Logistic Regression

Robust Cut-Off Value

Robust Deviance Residuals

Sayı

Makale detayları

Robust methods for detecting bad leverage point in logistic regression

Yazarlar: Ebru Gündoğan Aşık | Arzu Altin Yavuz | Zafer Küçük

Yayıncı: Yildiz Technical University

Ana Başlıklar: Biochemistry and Cell Biology (Other) | Clinical Chemistry

Dergi: Sigma Journal of Engineering and Natural Sciences Vol.42 Issue 5, pp.1344-1356

Belge Tipi: Makale

Yayın Tarihi: 4/2024

Belge Dili: İngilizce

Document ID (Org No.): 2001329131

pISSN: 1304-7191

Bad Leverage

High-Leverage

Logistic Regression

Robust Cut-Off Value

Robust Deviance Residuals

Sayı

Yazarlar:

Ebru Gündoğan Aşık | Arzu Altin Yavuz | Zafer Küçük

Yayıncı:

Yildiz Technical University

Ana Başlıklar:

Biochemistry and Cell Biology (Other) | Clinical Chemistry

Dergi:

Sigma Journal of Engineering and Natural Sciences Vol.42 Issue 5, pp.1344-1356

Belge Tipi:

Makale

Yayın Tarihi:

4/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001329131

pISSN:

1304-7191