Robust accelerated failure time regression
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 sc...
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2011
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| Acceso en línea: | https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_01679473_v55_n1_p874_Locatelli http://hdl.handle.net/20.500.12110/paper_01679473_v55_n1_p874_Locatelli |
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paper:paper_01679473_v55_n1_p874_Locatelli2023-06-08T15:17:10Z Robust accelerated failure time regression Accelerated failure time models Censoring Robust regression Intelligent systems Maximum likelihood Monte Carlo methods Accelerated failure time models Censored observations Censoring Conditional expectation High breakdown point Location-scale families Maximum likelihood estimate Robust regressions Maximum likelihood estimation 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 generated 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. © 2010 Elsevier B.V. All rights reserved. 2011 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_01679473_v55_n1_p874_Locatelli http://hdl.handle.net/20.500.12110/paper_01679473_v55_n1_p874_Locatelli |
| institution |
Universidad de Buenos Aires |
| institution_str |
I-28 |
| repository_str |
R-134 |
| collection |
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) |
| topic |
Accelerated failure time models Censoring Robust regression Intelligent systems Maximum likelihood Monte Carlo methods Accelerated failure time models Censored observations Censoring Conditional expectation High breakdown point Location-scale families Maximum likelihood estimate Robust regressions Maximum likelihood estimation |
| spellingShingle |
Accelerated failure time models Censoring Robust regression Intelligent systems Maximum likelihood Monte Carlo methods Accelerated failure time models Censored observations Censoring Conditional expectation High breakdown point Location-scale families Maximum likelihood estimate Robust regressions Maximum likelihood estimation Robust accelerated failure time regression |
| topic_facet |
Accelerated failure time models Censoring Robust regression Intelligent systems Maximum likelihood Monte Carlo methods Accelerated failure time models Censored observations Censoring Conditional expectation High breakdown point Location-scale families Maximum likelihood estimate Robust regressions Maximum likelihood estimation |
| description |
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 generated 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. © 2010 Elsevier B.V. All rights reserved. |
| title |
Robust accelerated failure time regression |
| title_short |
Robust accelerated failure time regression |
| title_full |
Robust accelerated failure time regression |
| title_fullStr |
Robust accelerated failure time regression |
| title_full_unstemmed |
Robust accelerated failure time regression |
| title_sort |
robust accelerated failure time regression |
| publishDate |
2011 |
| url |
https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_01679473_v55_n1_p874_Locatelli http://hdl.handle.net/20.500.12110/paper_01679473_v55_n1_p874_Locatelli |
| _version_ |
1768546110324867072 |