Reference: | Atkinson, A. C. (1994). ”Fast very robust methods for the detection o smultiple outliers”, Hournal of the American Association 89, 1329-1339. Atkinson, A. C. and Riani, M. (2001). ”Regression diagnostics for binomial data from the forward search”, The Statistician, 50, 63-78. Atkinson, A. C. and Riani, M. (2000). Robust Diagnostic Regression Analysis, New York: Springer. Beaton, A. E. (1964). ”The use of special matrix operations in statitical calculus”, Educational Testing Service Research Bulletin, RB, 64-52. Belsey, D. A., Kuh, E., and Welsch, R.E. (1980). Regression Diagnostics: Identifying In?uential Data and Sources of Collinearity, New York: Wiley. Bliss, C. I. (1935). ”The calculation of the dosage-mortality curve”, Annals of Applied Biology 22, 134-167. Christmann, A. (1994). ”Least median of weight squared in logistic regression with large strata”,Biometrika, 81, 413-417. Collett, D., (1991). Modelling Binary Data, London: Chapman & Hall. Cook, R. D., (1977). ”Detection of in?uential observations in linear regression”, Technometrics, 19, 15-18. Cook, R. D., and Weisberg, S., (1982). Residuals and In?uence in Regression, London: Chapman & Hall. Cook, R. D., and Weisberg, S., (1999). Applied Regression Including Computing and Graphics, New York: John Wiley & Sons. Croux, C., Flandre, C. and Haesbroeck,G. (2002). ”The breakdown behavior of the maximum likelihood estimator in the logistic regression”, Statistics & Probability Letters 60, 377- 386. Dempster, A. P. (1969). Elements of Continuous Multivariate Analysis. Addison-Wesley, Reading, MA. Dempster, A. P., Laird, N. M. and Rubin, D. B. (1997). ”Maximum likelihood from incomplete data via the EM algorithm (with discussion)”, J. Roy. Statist. Soc. 39, 1-38. Donoho, D. L., and Huber, P. J. (1983). The Notion of Breakdown Point. In A Festschrift for Erich L. Lehmann, Ed. P. J. Bickel, K. A. Docksum and J. L. Hodges, Jr., 157-84. Belmont CA: Wadsworth. Ibrahim, G. J. (1990). ”Incomplete data in generalized linear models”, American Statistical Association, 85, 765 - 769. Ibrahim, J. G. and Chen, M. H., Lipsitz, S. R., (1999). ”Monte Carlo EM for missing covariates in parametric regression models”, Biometrics, 55, 591 - 596. Little J. A. and Rubin D. B. (1987). Ststistical Analysis with Missing Data, New York: John Wiley & Sons. Little, J. A. and Schluchter, M. D. (1985). ”Maximum likelihood estimation for mixed continuous and categorical data with missing values”, Biometrika, 72, 497-512. Olkin, I., and Tate, R. F. (1961). ”Multivariate correlation models with mixed discrete and continuous variables”, Ann. Math. Statist., 32, 448-465. Pregibon, D. (1981). ”Logistic Regression Diagnostics”, The Annals of Statistic, 9 705-724. Rousseeuw, P. J. (1984). ”Least median of squares regression”, J. Am. Stat. Assoc., 79, 871-880. Rubin, D. B. (1976). ”Inference and missing data”, Biometrika 63, 581-592. Schafer, J. L. (1997). Analysis of Incomplete Multivariate Data, London: Chapman & Hall. Wei, G. C. and Tanner, M. A. (1990). ”A Monte Carlo implementation of the EM algorithm and the poor man’s data augmentation algorithm”. Journal of the American Statistical Association 85, 699-704. Zelterman, D. (1999). Models for Discrete Data, Oxford: Oxford University Press. |