Robust median estimator in logistic regression

Elsevier Science Bv 2023-06-20T09:43:19Z 2023-06-20T09:43:19Z 2008-12-01

Hobza, Pavel
Pardo Llorente, Leandro
Vajda, Igor

This 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.
Depto. de Estadística e Investigación Operativa
Fac. de Ciencias Matemáticas
TRUE
pub

519.233.5; logistic regression; MLE; Morgenthaler estimator; Bianco and Yohai estimator; Croux and Haselbroeck estimator; median estimator; consistency; asymptotic normality; robustness; Generalized linear-models; Nonlinear-regression; Fits; Estadística aplicada; Estadística matemática (Matemáticas); 1209 Estadística; journal article

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0378-3758
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ESRCM oai:docta.ucm.es:20.500.14352/50246
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0378-3758
10.1016/j.jspi.2008.02.010
1413948518

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