Artículo
Penalized robust estimators in sparse logistic regression
Fecha de publicación:
09/2022
Editorial:
Springer
Revista:
Test
ISSN:
1133-0686
Idioma:
Inglés
Tipo de recurso:
Artículo publicado
Clasificación temática:
Resumen
Sparse covariates are frequent in classification and regression problems where the task of variable selection is usually of interest. As it is well known, sparse statistical models correspond to situations where there are only a small number of nonzero parameters, and for that reason, they are much easier to interpret than dense ones. In this paper, we focus on the logistic regression model and our aim is to address robust and penalized estimation for the regression parameter. We introduce a family of penalized weighted M-type estimators for the logistic regression parameter that are stable against atypical data. We explore different penalization functions including the so-called Sign penalty. We provide a careful analysis of the estimators convergence rates as well as their variable selection capability and asymptotic distribution for fixed and random penalties. A robust cross-validation criterion is also proposed. Through a numerical study, we compare the finite sample performance of the classical and robust penalized estimators, under different contamination scenarios. The analysis of real datasets enables to investigate the stability of the penalized estimators in the presence of outliers.
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Articulos (IC)
Articulos de INSTITUTO DE CALCULO
Articulos de INSTITUTO DE CALCULO
Citación
Bianco, Ana Maria; Boente Boente, Graciela Lina; Chebi, Gonzalo; Penalized robust estimators in sparse logistic regression; Springer; Test; 31; 3; 9-2022; 563-594
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