Identification of multiple high leverage points in logistic regression

Journal of Applied Statistics

Volumn 40, Issue 12, 2013, Pages 2601-2616

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

^a BALL STATE UNIVERSITY   (United States)

^b AMERICAN UNIVERSITY IN CAIRO   (Egypt)

Author keywords

covariates; deletion median distance from the median; group deletion; high leverage points; logistic regression; masking; Monte Carlo simulation; robust regression; swamping

Indexed keywords

EID: 84885371513     PISSN: 02664763     EISSN: 13600532     Source Type: Journal    
DOI: 10.1080/02664763.2013.822057     Document Type: Article

References (22)

1. Andrews D. F., Pregibon D., Finding the outliers that matter, J. Roy. Stat. Soc. Ser. B 40 (1978), 85-93.
2. Barnett V. and Lewis, T. B., Outliers in Statistical Data, Wiley, New York, 1995.
3. Belsley D. A., Kuh E. and Welsch, R. E., Regression Diagnostics: Identifying Influential Data and Sources of Collinearity, Wiley, New York, 1980.
4. Billor N., Hadi A. S., Velleman F., BACON: Blocked adaptive computationally-efficient outlier nominator, Comput. Stat. Data Anal. 34 (2000), 279-298. doi: 10.1016/S0167-9473(99)00101-2
5. Brown B. W.Jr, Prediction analysis for binary data, Biostatistics CasebookMiller R. G.Jr, Efron B., Brown B. W.Jr and Moses, L. E.Wiley, New York, 19803-18.
6. Chatterjee S. and Hadi, A. S., Influential observations, high leverage points, and outliers in linear regression, Stat. Sci. 1 (1986), 379-393. doi: 10.1214/ss/1177013622
7. Cook R. D., Hawkins H., Comments on unmasking multivariate outliers and leverage points by P.J. Rousseeuw and B.C. van Zomeren, J. Am. Stat. Assoc. 85 (1990), 648-651.
8. Habshah M., Norazan R., Imon A. H.M.R., The performance of diagnostic-robust generalized potentials for the identification of multiple high leverage points in linear regression, J. Appl. Stat. 36 (2009), 507-520. doi: 10.1080/02664760802553463
9. Hadi A. S., A new measure of overall potential influence in linear regression, Comput. Stat. Data Anal. 14 (1992), 1-27. doi: 10.1016/0167-9473(92)90078-T
10. Hadi A. S., Imon A. H.M.R., Werner M., Detection of outliers, Wiley Int. Rev. Comput. Stat. 1 (2009), 57-70. doi: 10.1002/wics.6
11. Hoaglin D. C. and Welsch, R. E., The hat matrix in regression and ANOVA, Am. Stat. 32 (1978), 17-22.
12. Hocking R. R. and Pendleton, O. J., The regression dilemma, Comm. Stat. Theory Methods 12 (1983), 497-527. doi: 10.1080/03610928308828477
13. Hosmer D. W., Lemeshow S., Applied Logistic Regression, Wiley, New York, 1980.
14. Huber P. J., Robust Statistics, Wiley, New York, 1981.
15. Imon A. H.M.R., Identifying multiple influential observations in linear regression, J. Appl. Stat. 32 (2005), 929-946. doi: 10.1080/02664760500163599
16. Imon A. H.M.R., Identification of high leverage points in logistic regression, Pak. J. Stat. 22 (2006), 147-156.
17. Imon A. H.M.R. and Hadi, A. S., Identification of multiple outliers in logistic regression, Comm. Stat. Theory Methods. 37 (2008), 1967-1709.
18. Nurunnabi A. A.M., Imon A. H.M.R., Nasser M., Identification of multiple influential observations in logistic regression, J. Appl. Stat. 37 (2010), 1605-1624. doi: 10.1080/02664760903104307
19. Pregibon D., Logistic regression diagnostics, Ann. Stat. 9 (1981), 705-724. doi: 10.1214/aos/1176345513
20. Rousseeuw P. J. and Leroy, A. M., Robust Regression and Outlier Detection, Wiley, New York, 1987.
21. Ryan T. P., Modern Regression Methods, Wiley, New York, 1997.
22. Velleman P. F. and Welsch, R. E., Efficient computing in regression diagnostics, Am. Stat. 35 (1981), 234-242.