Robust estimators of accelerated failure time regression with generalized log-gamma errors
The generalized log-gamma (GLG) model is a very flexible family of distributions to analyze datasets in many different areas of science and technology. Estimators are proposed which are simultaneously highly robust and highly efficient for the parameters of a GLG distribution in the presence of cens...
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2017
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| Acceso en línea: | https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_01679473_v107_n_p92_Agostinelli http://hdl.handle.net/20.500.12110/paper_01679473_v107_n_p92_Agostinelli |
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paper:paper_01679473_v107_n_p92_Agostinelli |
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paper:paper_01679473_v107_n_p92_Agostinelli2023-06-08T15:17:06Z Robust estimators of accelerated failure time regression with generalized log-gamma errors Censored data Quantile distance estimates Truncated maximum likelihood estimators Weighted likelihood estimators τ estimators Intelligent systems Maximum likelihood Maximum likelihood estimation Mean square error Monte Carlo methods Sampling Accelerated failure time models Censored data Censored observations Error distributions Maximum likelihood estimator Quantile distance estimates Science and Technology Weighted likelihood estimators Errors The generalized log-gamma (GLG) model is a very flexible family of distributions to analyze datasets in many different areas of science and technology. Estimators are proposed which are simultaneously highly robust and highly efficient for the parameters of a GLG distribution in the presence of censoring. Estimators with the same properties for accelerated failure time models with censored observations and error distribution belonging to the GLG family are also introduced. It is proven that the proposed estimators are asymptotically fully efficient and the maximum mean square error is examined using Monte Carlo simulations. The simulations confirm that the proposed estimators are highly robust and highly efficient for a finite sample size. Finally, the benefits of the proposed estimators in applications are illustrated with the help of two real datasets. © 2016 Elsevier B.V. 2017 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_01679473_v107_n_p92_Agostinelli http://hdl.handle.net/20.500.12110/paper_01679473_v107_n_p92_Agostinelli |
| institution |
Universidad de Buenos Aires |
| institution_str |
I-28 |
| repository_str |
R-134 |
| collection |
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) |
| topic |
Censored data Quantile distance estimates Truncated maximum likelihood estimators Weighted likelihood estimators τ estimators Intelligent systems Maximum likelihood Maximum likelihood estimation Mean square error Monte Carlo methods Sampling Accelerated failure time models Censored data Censored observations Error distributions Maximum likelihood estimator Quantile distance estimates Science and Technology Weighted likelihood estimators Errors |
| spellingShingle |
Censored data Quantile distance estimates Truncated maximum likelihood estimators Weighted likelihood estimators τ estimators Intelligent systems Maximum likelihood Maximum likelihood estimation Mean square error Monte Carlo methods Sampling Accelerated failure time models Censored data Censored observations Error distributions Maximum likelihood estimator Quantile distance estimates Science and Technology Weighted likelihood estimators Errors Robust estimators of accelerated failure time regression with generalized log-gamma errors |
| topic_facet |
Censored data Quantile distance estimates Truncated maximum likelihood estimators Weighted likelihood estimators τ estimators Intelligent systems Maximum likelihood Maximum likelihood estimation Mean square error Monte Carlo methods Sampling Accelerated failure time models Censored data Censored observations Error distributions Maximum likelihood estimator Quantile distance estimates Science and Technology Weighted likelihood estimators Errors |
| description |
The generalized log-gamma (GLG) model is a very flexible family of distributions to analyze datasets in many different areas of science and technology. Estimators are proposed which are simultaneously highly robust and highly efficient for the parameters of a GLG distribution in the presence of censoring. Estimators with the same properties for accelerated failure time models with censored observations and error distribution belonging to the GLG family are also introduced. It is proven that the proposed estimators are asymptotically fully efficient and the maximum mean square error is examined using Monte Carlo simulations. The simulations confirm that the proposed estimators are highly robust and highly efficient for a finite sample size. Finally, the benefits of the proposed estimators in applications are illustrated with the help of two real datasets. © 2016 Elsevier B.V. |
| title |
Robust estimators of accelerated failure time regression with generalized log-gamma errors |
| title_short |
Robust estimators of accelerated failure time regression with generalized log-gamma errors |
| title_full |
Robust estimators of accelerated failure time regression with generalized log-gamma errors |
| title_fullStr |
Robust estimators of accelerated failure time regression with generalized log-gamma errors |
| title_full_unstemmed |
Robust estimators of accelerated failure time regression with generalized log-gamma errors |
| title_sort |
robust estimators of accelerated failure time regression with generalized log-gamma errors |
| publishDate |
2017 |
| url |
https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_01679473_v107_n_p92_Agostinelli http://hdl.handle.net/20.500.12110/paper_01679473_v107_n_p92_Agostinelli |
| _version_ |
1768546394630520832 |