Robust testing in the logistic regression model

Computational Statistics and Data Analysis

Volumn 53, Issue 12, 2009, Pages 4095-4105

  Bianco, Ana M ^a,b   Martínez, Elena ^a  

^a INSTITUTO DE CÁLCULO   (Argentina)

^b Facultad de Ciencias Naturales e Instituto M Lillo   (Argentina)

Author keywords

[No Author keywords available]

Indexed keywords

ASYMPTOTIC DISTRIBUTIONS; COMPUTER INTENSIVE METHODS; DATA ANALYSIS; EMPIRICAL STUDIES; LECTURE NOTES; LOGISTIC REGRESSION MODELS; LOGISTIC REGRESSIONS; MONTE CARLO STUDY; NEW YORK; P-VALUES; ROBUST ESTIMATION; ROBUST STATISTICS; TESTING HYPOTHESIS; WEIGHT FUNCTIONS;

DISTRIBUTION FUNCTIONS; ESTIMATION; FUNCTION EVALUATION; LOGISTICS; STATISTICAL TESTS;

REGRESSION ANALYSIS;

EID: 69449105982     PISSN: 01679473     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.csda.2009.04.015     Document Type: Article

References (20)

1. Bianco, A.M., Martínez, E.J., 2009. Robust testing in the logistic regression model. Available at http://www.ic.fcen.uba.ar/preprints/
2. Bianco A.M., and Yohai V.J. Robust estimation in the logistic regression model. In: Rieder H. (Ed). Robust Statistics, Data Analysis, and Computer Intensive Methods. Lecture Notes in Statistics vol. 109 (1996), Springer Verlag, New York 17-34
3. Bondell H.D. Minimum distance estimation for the logistic regression model. Biometrika 92 (2005) 724-731
4. Bondell H.D. A characteristic function approach to the biased sampling model, with application to robust logistic regression. Journal of Statistical Planning and Inference 138 (2008) 742-755
5. Cantoni E., and Ronchetti E. Robust inference for generalized linear models. Journal of the American Statistical Association 96 (2001) 1022-1030
6. Carroll R.J., and Pederson S. On robust estimation in the logistic regression model. Journal of the Royal Statistical Society B 55 (1993) 693-706
7. Christmann A. Least median of weighted squares in logistic regression with large strata. Biometrika 81 (1994) 413-417
8. Croux C., Haesbroeck G., and Joossens K. Logistic discrimination using robust estimators: An influence function approach. The Canadian Journal of Statistics 36 2 (2008) 157-174
9. Croux C., Flandre C., and Haesbroeck G. The breakdown behavior of the maximum likelihood estimator in the logistic regression model. Statistics and Probability Letters 60 (2002) 377-386
10. Croux C., and Haesbroeck G. Implementing the Bianco and Yohai estimator for logistic regression. Computational Statististics and Data Analysis 44 (2003) 273-295
11. Finney D.J. The estimation from individual records of the relationship between dose and quantal response. Biometrika 34 (1947) 320-334
12. Hauck W.W., and Donner A. Wald's test as applied to hypotheses in logit analysis. Journal of the American Statistical Association 72 (1977) 851-853
13. Heritier S., and Ronchetti E. Robust bounded-influence tests in general parametric models. Journal of the American Statistical Association 89 (1994) 897-904
14. Künsch H., Stefanski L., and Carroll R. Conditionally unbiased bounded influence estimation in general regression models, with applications to generalized linear models. Journal of the American Statistical Association 84 (1989) 460-466
15. Markatou M., and He X. Bounded influence, high breakdown point testing procedures in linear models. Journal of the American Statistical Association 89 (1994) 543-549
16. Morgenthaler S. Least-absolute-deviations fits for generalized linear model. Biometrika 79 (1992) 747-754
17. Pregibon D. Logistic regression diagnostics. Annals of Statistics 9 (1981) 705-724
18. Pregibon D. Resistant fits for some commonly used logistic models with medical applications. Biometrics 38 (1982) 485-498
19. Rousseeuw P.J. Multivariate estimation with high breakdown point. In: Grossmann W., Pflug G., Vincze I., and Wertz W. (Eds). Mathematical Statistics and Applications vol. B (1985), Reidel, Dordrecht 283-297
20. Stefanski L., Carroll R., and Ruppert D. Optimally bounded score functions for generalized linear models with applications to logistic regression. Biometrika 73 (1986) 413-424