Abstract:
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.
Registro:
Documento: |
Artículo
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Título: | Robust estimators of accelerated failure time regression with generalized log-gamma errors |
Autor: | Agostinelli, C.; Locatelli, I.; Marazzi, A.; Yohai, V.J. |
Filiación: | Department of Mathematics, University of Trento, Trento, Italy Institute of social and preventive medicine, Lausanne University Hospital, Switzerland Nice Computing SA, Ch. de Maillefer 37, Le Mont/Lausanne, CH-1052, Switzerland Departamento de Matematicas, Facultad de Ciencias Exactas y Naturales, University of Buenos Aires and CONICET, Argentina
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Palabras clave: | 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 |
Año: | 2017
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Volumen: | 107
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Página de inicio: | 92
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Página de fin: | 106
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DOI: |
http://dx.doi.org/10.1016/j.csda.2016.10.012 |
Título revista: | Computational Statistics and Data Analysis
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Título revista abreviado: | Comput. Stat. Data Anal.
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ISSN: | 01679473
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CODEN: | CSDAD
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Registro: | https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_01679473_v107_n_p92_Agostinelli |
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Citas:
---------- APA ----------
Agostinelli, C., Locatelli, I., Marazzi, A. & Yohai, V.J.
(2017)
. Robust estimators of accelerated failure time regression with generalized log-gamma errors. Computational Statistics and Data Analysis, 107, 92-106.
http://dx.doi.org/10.1016/j.csda.2016.10.012---------- CHICAGO ----------
Agostinelli, C., Locatelli, I., Marazzi, A., Yohai, V.J.
"Robust estimators of accelerated failure time regression with generalized log-gamma errors"
. Computational Statistics and Data Analysis 107
(2017) : 92-106.
http://dx.doi.org/10.1016/j.csda.2016.10.012---------- MLA ----------
Agostinelli, C., Locatelli, I., Marazzi, A., Yohai, V.J.
"Robust estimators of accelerated failure time regression with generalized log-gamma errors"
. Computational Statistics and Data Analysis, vol. 107, 2017, pp. 92-106.
http://dx.doi.org/10.1016/j.csda.2016.10.012---------- VANCOUVER ----------
Agostinelli, C., Locatelli, I., Marazzi, A., Yohai, V.J. Robust estimators of accelerated failure time regression with generalized log-gamma errors. Comput. Stat. Data Anal. 2017;107:92-106.
http://dx.doi.org/10.1016/j.csda.2016.10.012