Hobza, PavelPardo Llorente, LeandroVajda, Igor2023-06-202023-06-202008-12-010378-375810.1016/j.jspi.2008.02.010https://hdl.handle.net/20.500.14352/50246This paper introduces a median estimator of the logistic regression parameters. It is defined as the classical L-1-estimator applied to continuous data Z(1),..., Z(n) obtained by a statistical smoothing of the original binary logistic regression observations Y-1,..., Y-n. Consistency and asymptotic normality of this estimator are proved. A method called enhancement is introduced which in some cases increases the efficiency of this estimator. Sensitivity to contaminations and leverage points is studied by simulations and compared in this manner with the sensitivity of some robust estimators previously introduced to the logistic regression. The new estimator appears to be more robust for larger sample sizes and higher levels of contamination.engRobust median estimator in logistic regressionjournal articlehttp://www.sciencedirect.com/science/article/pii/S0378375808001407http://www.sciencedirect.com/restricted access519.233.5logistic regressionMLEMorgenthaler estimatorBianco and Yohai estimatorCroux and Haselbroeck estimatormedian estimatorconsistencyasymptotic normalityrobustnessGeneralized linear-modelsNonlinear-regressionFitsEstadística aplicadaEstadística matemática (Matemáticas)1209 Estadística