Identification of multiple influential observations in logistic regression

Journal of Applied Statistics

Volumn 37, Issue 10, 2010, Pages 1605-1624

  Nurunnabi, A A M ^a   Imon, A H M Rahmatullah ^b   Nasser, M ^c  

^a Uttara University   (Bangladesh)

^b BALL STATE UNIVERSITY   (United States)

^c UNIVERSITY OF RAJSHAHI   (Bangladesh)

Author keywords

Generalized DFFITS; Generalized Studentized Pearson residual; Generalized weight; High leverage point; Influential observation; Masking; Outlier; Swamping

Indexed keywords

EID: 77956946271     PISSN: 02664763     EISSN: 13600532     Source Type: Journal    
DOI: 10.1080/02664760903104307     Document Type: Article

References (24)

1. A.C. Atkinson and M. Riani, Robust Diagnostic Regression Analysis, Springer-Verlag, NewYork, 2000.
2. D.A. Belsley, E. Kuh, and R.E. Welsch, Regression Diagnostics: Identifying Influential Data and Sources of Collinearity, Wiley, NewYork, 1980.
3. N. Billor, A.S. Hadi, and F. Vellman, BACON: Blocked adaptive computationally efficient outlier nominator, Computat. Stat. Data Anal. 34 (2000), pp. 279-298.
4. B.W. Brown Jr., Prediction analysis for binary data, in Biostatistics Casebook, R.G. Miller, Jr. B. Efron, B.W. Brown, Jr., and L.E. Moses, eds., pp. 3-18, Wiley, NewYork, 1980.
5. J. M. Chambers and T. J. Hastie, Statistical Models in S, Wadsworth and Brooks, Pacific Grove, CA, 1992.
6. S. Chatterjee and A.S. Hadi, Sensitivity Analysis in Linear Regression, Wiley, NewYork, 1988.
7. S. Chatterjee and A.S. Hadi, Regression Analysis by Examples, 4th ed., Wiley, NewYork, 2006.
8. R.D. Cook, Detection of influential observations in linear regression, Technometrics 19 (1977), pp. 15-18.
9. R.D. Cook, Assessment of local influence, J. Roy. Stat. Soc., Ser-B 48 (1986), pp. 131-169.
10. R.D. Cook and S. Weisberg, Residuals and Influence in Regression, Chapman and Hall, London, 1982.
11. P. Davies, A.H.M.R. Imon, and M.M. Ali, A conditional expectation method for improved residual estimation and outlier identification in linear regression, Int. J. Stat. Sci., special issue in honour of Professor M.S. Huq (2004), pp. 191-208.
12. D.J. Finney, The estimation from individual records of the relationship between dose and quantile response, Biometrika 34 (1947), pp. 320-334.
13. A.S. Hadi, A new measure of overall potential influence in linear regression, Comput. Stat. Data Anal. 14 (1992), pp. 1-27.
14. A.S. Hadi and J.S. Simonoff, Procedure for the identification of outliers in linear models, J. Am. Stat. Assoc. 88 (1993), pp. 1264-1272.
15. D.W. Hosmer and S. Lemeshow, Applied Logistic Regression, Wiley, NewYork, 2000.
16. A.H.M.R. Imon, Identifying multiple influential observations in linear regression, J. Appl. Stat. 32 (2005), pp. 929-946.
17. A.H.M.R. Imon, Identification of high leverage points in logistic regression, Pak. J. Stat. 22 (2006), pp. 147-156.
18. A.H.M.R. Imon and A.S. Hadi, Identification of multiple outliers in logistic regression, Comm. Stat., Theory and Methods 37 (2008), pp. 1697-1709.
19. A.J. Lawrance, Local and deletion influence in Directions in Robust Statistics and Diagnostics, Part I, W. Stahel and S. Weisberg, eds., pp. 141-157, Springer, Berlin, 1991.
20. W.Y. Poon and Y.S. Poon, Conformal normal curvature and assessment of local influence, J. Roy. Stat. Soc., Ser-B 61 (1999), pp. 51-56.
21. D. Pregibon, Logistic regression diagnostics, Ann. Stat. 9 (1981), pp. 977-986.
22. P.J. Rousseeuw and A. Leroy, Robust Regression and Outlier Detection, Wiley, NewYork, 1987.
23. T.P. Ryan, Modern Regression Methods, Wiley, NewYork, 1997.
24. V.J. Yohai, High breakdown point and high efficiency estimates for regression, Ann. Stat., 15 (1987), pp. 642-656.