[2] A. M. Bianco, V. J. Yohai:
Robust estimation in the logistic regression model. In: Robust Statistics, Data Analysis, and Computer Intensive Methods (Schloss Thurnau, 1994), pp. 17-34. Lecture Notes in Statist. 109 Springer, New York 1996.
MR 1491394 |
Zbl 0839.62030
[4] J. E. Dennis, Jr., R. B. Schnabel:
Numerical Methods for Unconstrained Optimization and Nonlinear Equations. Prentice-Hall, Englewood Cliffs, New Jersey 1983.
MR 0702023 |
Zbl 0847.65038
[6] F. R. Hampel, P. J. Rousseeuw, E. M. Ronchetti, W. A. Stahel:
Robust Statistics: The Approach Based on Influence Functions. Wiley, New York 1986.
MR 0829458 |
Zbl 0733.62038
[7] T. Hobza, L. Pardo, I. Vajda:
Median Estimators in Generalized Logistic Regression. Research Report DAR-UTIA 2005/40. Institute of Information Theory, Prague 2005 (available at
http://dar.site.cas.cz/?publication=1007)
[8] T. Hobza, L. Pardo, I. Vajda:
Robust Median Estimators in Logistic Regression. Research Report DAR-UTIA 2006/31. Institute of Information Theory, Prague 2006 (available at
http://dar.site.cas.cz/?publication=1089)
[13] F. Liese, I. Vajda:
A general asymptotic theory of $M$-estimators I. Math. Methods Statist. 12 (2003) 454-477.
MR 2054158
[14] F. Liese, I. Vajda:
A general asymptotic theory of $M$-estimators II. Math. Methods Statist. 13 (2004) 82-95.
MR 2078314 |
Zbl 1185.62053
[17] J. Moré, G. Burton, H. Kenneth: User Guide for MINPACK-1. Argonne National Laboratory Report ANL-80-74, Argonne 1980.
[19] J. Nagler:
Scobit: An alternative estimator to logit and Probit. Amer. J. Political Sci. 38 (1994), 1, 230-255.
DOI 10.2307/2111343
[20] D. Pregibon:
Resistant lits for some commonly used logistic models with medical applications. Biometrics 38 (1982), 485-498.
DOI 10.2307/2530463
[21] R. L. Prentice: A generalization of the probit and logit methods for dose-response curves. Biometrika 32 (1976), 761-768.