Identification of multiple outliers in logistic regression

Communications in Statistics - Theory and Methods

Volumn 37, Issue 11, 2008, Pages 1697-1709

  Imon, A H M Rahmatullah ^a   Hadi, Ali S ^b  

^a UNIVERSITY PUTRA MALAYSIA   (Malaysia)

^b The American University in Cairo   (Egypt)

Author keywords

Generalized standardized Pearson residuals; Group deletion; Logistic regression; Masking; Outliers; Pearson residuals

Indexed keywords

DATA REDUCTION; IDENTIFICATION (CONTROL SYSTEMS); MATHEMATICAL MODELS; NUMERICAL METHODS; PROBLEM SOLVING;

GENERALIZED STANDARDIZED PEARSON RESIDUALS; GROUP DELETION; LOGISTIC REGRESSION;

LINEAR REGRESSION;

EID: 41849136476     PISSN: 03610926     EISSN: 1532415X     Source Type: Journal    
DOI: 10.1080/03610920701826161     Document Type: Conference Paper

References (21)

1. Atkinson, A. C. (1994). Fast very robust methods for the detection of multiple outliers. J. Amer. Statist. Asso. 89:1329-1339.
2. Barnett, V., Lewis, T. B. (1994). Outliers in Statistical Data. 3rd ed. New York: Wiley.
3. Billor, N., Hadi, A. S., Velleman, F. (2000). BACON: Blocked adaptive computationally-efficient outlier nominator. Computat. Statist. Data Anal. 34:279-298.
4. Brown, B. W., Jr. (1980). Prediction analysis for binary data. In: Miller, R. G. Jr., Efron, B., Brown, B. W. Jr., Moses, L. E., eds. Biostatistics Casebook. New York: Wiley.
5. Chatterjee, S., Hadi, A. S. (1988). Sensitivity Analysis in Linear Regression. New York: Wiley.
6. Chen, C., Liu, L. M. (1993). Joint estimation of model parameters and outlier effects in time series. J. Amer. Statist. Asso. 88:284-297.
7. Cook, R. D., Hawkins, D. M. (1990). Comment on unmasking multivariate outliers and leverage points by Rousseeuw, P. J., van Zomeren, B. C. J. Amer. Statist. Assoc. 85:640-644.
8. Davies, P., Imon, A. H. M. R., Ali, M. M. (2004). A conditional expectation method for improved residual estimation and outlier identification in linear regression. Int. J. Statist. Sci. (Special issue in honour of Professor M. S. Haq). 191-208.
9. Finney, D. J. (1947). The estimation from individual records of the relationship between dose and quantal response. Biometrika 34:320-334.
10. Gentleman, J. F., Wilk, M. B. (1975). Detecting outliers in a two-way table: II supplementing the direct analysis of residuals. Biometrics 31:387-410.
11. Hadi, A. S. (1992). A new measure of overall potential influence in linear regression. Computat. Statist. Data Anal. 14:1-27.
12. Hadi, A. S. (2006). On the Visualization of Massive, Hyperdimensional Data. Seminar session, Department of Statistics, University of Rajshahi, Bangladesh.
13. Hadi, A. S., Simonoff, J. S. (1993). Procedure for the identification of outliers in linear models. J. Amer. Statist. Asso. 88:1264-1272.
14. Hampel, F. R., Ronchetti, E. M., Rousseeuw, P. J., Stahel, W. A. (1986). Robust Statistics: The Approach Based on Influence Function. New York: Wiley.
15. Hosmer, D. W., Lemeshow, S. (2000). Applied Logistic Regression. 2nd ed. New York: Wiley.
16. Imon, A. H. M. R. (2005). Identifying multiple influential observations in linear regression. J. Appl. Statist. 32:73-90.
17. Munier, S. (1999). Multiple outlier detection in logistic regression. Student 3:117-126.
18. Pregibon, D. (1981). Logistic regression diagnostics. Ann. Statist. 9:977-986.
19. Rousseeuw, P. J. (1984). Least median of squares regression. J. Amer. Statist. Assoc. 79:871-880.
20. Rousseeuw, P. J., Leroy, A. (1987). Robust Regression and Outlier Detection. New York: Wiley.
21. Ryan, T. P. (1997). Modern Regression Methods. New York: Wiley.