Subscribe Now Subscribe Today
Science Alert
 
Blue
   
Curve Top
Journal of Applied Sciences
  Year: 2010 | Volume: 10 | Issue: 23 | Page No.: 3042-3050
DOI: 10.3923/jas.2010.3042.3050
 
Facebook Twitter Digg Reddit Linkedin StumbleUpon E-mail

Robust Logistic Diagnostic for the Identification of High Leverage Points in Logistic Regression Model

B.A. Syaiba and M. Habshah

Abstract:
High leverage points are observations that have outlying values in covariate space. In logistic regression model, the identification of high leverage points becomes essential due to their gross effects on the parameter estimates. Currently, the distance from the mean diagnostic method is used to identify the high leverage points. The main limitation of the distance from the mean diagnostic method is that it tends to swamp some low leverage points even though it can identify the high leverage points correctly. In this study, we propose a new diagnostic method for the identification of high leverage points. Robust approach is firstly used to identify suspected high leverage points by computing the robust mahalanobis distance based on minimum volume ellipsoid or minimum covariance determinant estimators. For confirmation, the diagnostic procedure is used by computing the group deleted potential. We called this proposed diagnostic method the robust logistic diagnostic. The performance of the proposed diagnostic method is then investigated through real examples and monte carlo simulation study. The result of this study indicates that the proposed diagnostic method ensures only correct high leverage points are identified and free from swamping and masking effects.
PDF Fulltext XML References Citation Report Citation
 RELATED ARTICLES:
  •    Modified Standardized Pearson Residual for the Identification of Outliers in Logistic Regression Model
How to cite this article:

B.A. Syaiba and M. Habshah, 2010. Robust Logistic Diagnostic for the Identification of High Leverage Points in Logistic Regression Model. Journal of Applied Sciences, 10: 3042-3050.

DOI: 10.3923/jas.2010.3042.3050

URL: https://scialert.net/abstract/?doi=jas.2010.3042.3050

COMMENT ON THIS PAPER
 
 
 

 

 
 
 
 
 
 
 
 
 

 
 
 
 
 
 
 

Curve Bottom