Robust accelerated failure time regression

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Etat: Public
Version: de l'auteur
ID Serval
serval:BIB_BACF1CC59855
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Institution
Titre
Robust accelerated failure time regression
Périodique
Computational Statistics and Data Analysis
Auteur(s)
Locatelli Isabella, Marazzi Alfio, Yohai Victor J.
ISSN
0167-9473
Statut éditorial
Publié
Date de publication
2011
Peer-reviewed
Oui
Volume
55
Numéro
1
Pages
874-887
Langue
anglais
Résumé
Robust estimators for accelerated failure time models with asymmetric (or symmetric) error distribution and censored observations are proposed. It is assumed that the error model belongs to a log-location-scale family of distributions and that the mean response is the parameter of interest. Since scale is a main component of mean, scale is not treated as a nuisance parameter. A three steps procedure is proposed. In the first step, an initial high breakdown point S estimate is computed. In the second step, observations that are unlikely under the estimated model are rejected or down weighted. Finally, a weighted maximum likelihood estimate is computed. To define the estimates, functions of censored residuals are replaced by their estimated conditional expectation given that the response is larger than the observed censored value. The rejection rule in the second step is based on an adaptive cut-off that, asymptotically, does not reject any observation when the data are generat ed according to the model. Therefore, the final estimate attains full efficiency at the model, with respect to the maximum likelihood estimate, while maintaining the breakdown point of the initial estimator. Asymptotic results are provided. The new procedure is evaluated with the help of Monte Carlo simulations. Two examples with real data are discussed.
Mots-clé
Accelerated failure time models, Robust regression, Censoring, Censored-data, Model
Web of science
Création de la notice
30/11/2010 16:16
Dernière modification de la notice
20/08/2019 15:28
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