Reference: | Andrews, D. F. and D. Pregibon. (1978). Finding the outliers that matter. Journal of the Royal Statistical Society, Series B40, 85-94.
Belsley, D. A., E. Kuh. and R. E. Welsch. (1980). Regression Diagnostics: Identifying Influential Data and Sources of Collinearity. John Wiley and Sons, New York.
Christensen, R. (1997). Log-linear Models and Logistic Regression. Springer-Verlag, New York.
Collett, D. (1991). Modelling Binary Data. Chapman and Hall, London.
Cook, R. D. (1977). Detection of influential observations in linear regression. Technometrics, 19, 15-18.
Cook, R. D. (1979). Influential observations in linear regression. Journal of the American statistical Association, 74, 169-174.
Copas, J. B. (1988). Binary regression models for contaminated data (with discussion). Journal of the Royal Statistical Society, Series B50, 225-265.
Fowlkes, E. B. (1987). Some diagnostics for binary regression via smoothing. Biometrika, 74, 503-505.
Hoaglin, D. C. and R. E. Welsch. (1978). The hat matrix in regression and ANOVA. The American Statistician, 32, 17-22.
Hosmer, D. W. and S. Lemeshow. (1980). A goodness-of-fit test for the multiple logistic regression model. Communications in Statistics. A9(10), 1043-1069.
Hosmer, D. W. and S. Lemeshow. (1989). Applied Logistic Regression. John Wiley and Sons, New York.
Hosmer, D. W., S. Taber, and S. Lemeshow. (1991). The importance of assessing the fit of logistic regression models: a case study. American Journal of Public Health, 81, 1630-1635.
Jennings, D. E. (1986). Outliers and residual distributions in logistic regression. Journal of the American Statistical Association, 81, 987-990.
Kay, R. and S. Little. (1986). Assessing the fit of the logistic model: a case study of children with the haemolytic uraemic syndrome. Applied Statistics, 35, 16-30.
Kim, C. and K. Jeong. (1993). On the logistic regression diagnostics. Journal of the korean Statistical Society, 22, 27-37.
Landwehr, J. M., D. Pergibon, and A. C. Shoemaker. (1984). Graphical methods for assessing logistic regression models. Journal of the American statistical Association, 79, 61-71.
Pregibon, D. (1981). Logistic regression diagnostics. Annals of Statistics, 9, 705-724.
Ryan, T. P. (1996). Modern Regression Methods. John Wiley and Sons, New York.
Wang, P. C. (1987). Residual plots for detecting nonlinearity in generalized linear models. Technometrics, 29, 435-438. |