Robust Box-Cox transformations for simple regression

Détails

ID Serval
serval:BIB_30F92C0D8FC4
Type
Partie de livre
Sous-type
Chapitre: chapitre ou section
Collection
Publications
Institution
Titre
Robust Box-Cox transformations for simple regression
Titre du livre
Theory and applications of recent robust methods
Auteur⸱e⸱s
Marazzi Alfio, Yohai Victor J.
Editeur
Birkhäuser
Lieu d'édition
Basel
Statut éditorial
Publié
Date de publication
2004
Editeur⸱rice scientifique
Hubert Mia, et al.
Pages
173-182
Langue
anglais
Résumé
The use of the Box-Cox family of transformations is a popular approach to make data behave according to a linear regression model. The regression coefficients, as well as the parameter A defining the transformation, are generally estimated by maximum likelihood, assuming homoscedastic normal errors. These estimates are nonrobust; in addition, consistency to the true parameters holds only if the assumptions of normality and homoscedasticity are satisfied. We present here a new class of estimates, for the case of simple regression, which are robust and consistent even if the assumptions of normality and homoscedasticity do not hold.
Mots-clé
Statistics as Topic , Linear Models
Création de la notice
06/03/2008 10:40
Dernière modification de la notice
20/08/2019 14:15
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