Artículo

Agostinelli, C.; Valdora, M.; Yohai, V.J."Initial robust estimation in generalized linear models" (2019) Computational Statistics and Data Analysis. 134:144-156
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Abstract:

Generalized Linear Models are routinely used in data analysis. Classical estimators are based on the maximum likelihood principle and it is well known that the presence of outliers can have a large impact on them. Several robust procedures have been presented in the literature, being redescending M-estimators the most widely accepted. Based on non-convex loss functions, these estimators need a robust initial estimate, which is often obtained by subsampling techniques. However, as the number of unknown parameters increases, the number of subsamples needed in order for this method to be robust, soon makes it infeasible. Furthermore the subsampling procedure provides a non deterministic starting point. A new method for computing a robust initial estimator is proposed. This method is deterministic and demands a relatively short computational time, even for large numbers of covariates. The proposed method is applied to M-estimators based on transformations. In addition, an iteratively reweighted least squares algorithm is proposed for the computation of the final estimates. The new methods are studied by means of Monte Carlo experiments. © 2018 Elsevier B.V.

Registro:

Documento: Artículo
Título:Initial robust estimation in generalized linear models
Autor:Agostinelli, C.; Valdora, M.; Yohai, V.J.
Filiación:Department of Mathematics, University of Trento, Trento, Italy
Departamento de Matematicas and Instituto de Cálculo, Facultad de Ciencias Exactas y Naturales, University of Buenos Aires, Argentina
Departamento de Matematicas and Institituto de Cálculo, Facultad de Ciencias Exactas y Naturales, University of Buenos Aires, CONICET, Argentina
Palabras clave:Initial estimates; Least squares estimators; M-estimators; Outliers; Poisson regression; Variance stabilizing transformations; Maximum likelihood estimation; Monte Carlo methods; Statistics; Initial estimate; Least-squares estimator; M-estimators; Outliers; Poisson regression; Iterative methods
Año:2019
Volumen:134
Página de inicio:144
Página de fin:156
DOI: http://dx.doi.org/10.1016/j.csda.2018.12.010
Handle:http://hdl.handle.net/20.500.12110/paper_01679473_v134_n_p144_Agostinelli
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_v134_n_p144_Agostinelli

Referencias:

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Citas:

---------- APA ----------
Agostinelli, C., Valdora, M. & Yohai, V.J. (2019) . Initial robust estimation in generalized linear models. Computational Statistics and Data Analysis, 134, 144-156.
http://dx.doi.org/10.1016/j.csda.2018.12.010
---------- CHICAGO ----------
Agostinelli, C., Valdora, M., Yohai, V.J. "Initial robust estimation in generalized linear models" . Computational Statistics and Data Analysis 134 (2019) : 144-156.
http://dx.doi.org/10.1016/j.csda.2018.12.010
---------- MLA ----------
Agostinelli, C., Valdora, M., Yohai, V.J. "Initial robust estimation in generalized linear models" . Computational Statistics and Data Analysis, vol. 134, 2019, pp. 144-156.
http://dx.doi.org/10.1016/j.csda.2018.12.010
---------- VANCOUVER ----------
Agostinelli, C., Valdora, M., Yohai, V.J. Initial robust estimation in generalized linear models. Comput. Stat. Data Anal. 2019;134:144-156.
http://dx.doi.org/10.1016/j.csda.2018.12.010