Robust accelerated failure time regression

TitreRobust accelerated failure time regression
Publication TypeJournal Article
Year of Publication2011
AuthorsLocatelli, I, Marazzi, A, Yohai, VJ
JournalComputational Statistics and Data Analysis
Volume55
Issue1
Pagination874-887
URLhttp://my.unil.ch/serval/document/BIB_BACF1CC59855.pdf
DOI10.1016/j.csda.2010.07.017
ISSN0167-9473
Accession NumberISI:000283017900074
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.

Notes

oai:serval.unil.ch:BIB_BACF1CC59855

Citation Key / SERVAL ID5269
Peer reviewOui

                         

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