Robust Estimators of the Generalized Log-Gamma Distribution

Détails

ID Serval
serval:BIB_FBA5F2181EC3
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Institution
Titre
Robust Estimators of the Generalized Log-Gamma Distribution
Périodique
Technometrics
Auteur(s)
Agostinelli C., Marazzi A., Yohai V.J.
ISSN
0040-1706 (Print)
ISSN-L
1537-2723 (Online)
Statut éditorial
Publié
Date de publication
2014
Peer-reviewed
Oui
Volume
56
Numéro
1
Pages
92-101
Langue
anglais
Résumé
We propose robust estimators of the generalized log-gamma distribution and, more generally, of location-shape-scale families of distributions. A (weighted) Q tau estimator minimizes a tau scale of the differences between empirical and theoretical quantiles. It is n(1/2) consistent; unfortunately, it is not asymptotically normal and, therefore, inconvenient for inference. However, it is a convenient starting point for a one-step weighted likelihood estimator, where the weights are based on a disparity measure between the model density and a kernel density estimate. The one-step weighted likelihood estimator is asymptotically normal and fully efficient under the model. It is also highly robust under outlier contamination. Supplementary materials are available online.
Web of science
Création de la notice
14/04/2014 10:27
Dernière modification de la notice
20/08/2019 17:26
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