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Robust median estimator in logistic regression



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Hobza, Pavel y Pardo Llorente, Leandro y Vajda, Igor (2008) Robust median estimator in logistic regression. Journal of Statistical Planning and Inference, 138 (12). pp. 3822-3840. ISSN 0378-3758

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URL Oficial: http://www.sciencedirect.com/science/article/pii/S0378375808001407

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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.

Tipo de documento:Artículo
Palabras clave: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
Materias:Ciencias > Matemáticas > Estadística aplicada
Ciencias > Matemáticas > Estadística matemática
Código ID:17513
Depositado:20 Dic 2012 11:08
Última Modificación:26 Jul 2018 10:56

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