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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
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
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
Volumen:107
Página de inicio:92
Página de fin:106
DOI: http://dx.doi.org/10.1016/j.csda.2016.10.012
Título revista:Computational Statistics and Data Analysis
Título revista abreviado:Comput. Stat. Data Anal.
ISSN:01679473
CODEN:CSDAD
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