Robust accelerated failure time regression

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State: Public
Version: author
Serval ID
serval:BIB_BACF1CC59855
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Robust accelerated failure time regression
Journal
Computational Statistics and Data Analysis
Author(s)
Locatelli Isabella, Marazzi Alfio, Yohai Victor J.
ISSN
0167-9473
Publication state
Published
Issued date
2011
Peer-reviewed
Oui
Volume
55
Number
1
Pages
874-887
Language
english
Abstract
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.
Keywords
Accelerated failure time models, Robust regression, Censoring, Censored-data, Model
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Create date
30/11/2010 16:16
Last modification date
20/08/2019 15:28
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