Parametrizations, fixed and random effects
Type de document :
Article dans une revue scientifique
URL permanente :
Titre :
Parametrizations, fixed and random effects
Auteur(s) :
Dermoune, Azzouz [Auteur]
Laboratoire Paul Painlevé - UMR 8524 [LPP]
Preda, Cristian [Auteur]
Laboratoire Paul Painlevé - UMR 8524 [LPP]
Laboratoire Paul Painlevé - UMR 8524 [LPP]
Preda, Cristian [Auteur]
Laboratoire Paul Painlevé - UMR 8524 [LPP]
Titre de la revue :
Journal of Multivariate Analysis
Éditeur :
Elsevier
Date de publication :
2016-11
ISSN :
0047-259X
Mot(s)-clé(s) :
General linear model
Fixed effect
Random effect
Cubic spline
Smoothing parameter
Likelihood
Climate change detection
Fixed effect
Random effect
Cubic spline
Smoothing parameter
Likelihood
Climate change detection
Discipline(s) HAL :
Statistiques [stat]/Applications [stat.AP]
Résumé en anglais : [en]
We consider the problem of estimating the random element s of a finite dimensional vector space S from the continuous data corrupted by noise with unknown variance σ 2 w. The mean E(s) (the fixed effect) of s belongs to a ...
Lire la suite >We consider the problem of estimating the random element s of a finite dimensional vector space S from the continuous data corrupted by noise with unknown variance σ 2 w. The mean E(s) (the fixed effect) of s belongs to a known vector subspace F of S, and the likelihood of the centred component s − E(s) (the random effect) belongs to an unknown supplementary space E of F relative to S and has the PDF proportional to exp{−q(s)\/2σ 2 s }, where σ 2 s is some unknown positive parameter. We introduce the notion of bases separating the fixed and random effects and define comparison criteria between two separating bases using the partition functions and the maximum likelihood method. We illustrate our results for climate change detection using the set S of cubic splines. We show the influence of the choice of separating basis on the estimation of the linear tendency of the temperature and the signal-to-noise ratio σ 2 w \/σ 2 s .Lire moins >
Lire la suite >We consider the problem of estimating the random element s of a finite dimensional vector space S from the continuous data corrupted by noise with unknown variance σ 2 w. The mean E(s) (the fixed effect) of s belongs to a known vector subspace F of S, and the likelihood of the centred component s − E(s) (the random effect) belongs to an unknown supplementary space E of F relative to S and has the PDF proportional to exp{−q(s)\/2σ 2 s }, where σ 2 s is some unknown positive parameter. We introduce the notion of bases separating the fixed and random effects and define comparison criteria between two separating bases using the partition functions and the maximum likelihood method. We illustrate our results for climate change detection using the set S of cubic splines. We show the influence of the choice of separating basis on the estimation of the linear tendency of the temperature and the signal-to-noise ratio σ 2 w \/σ 2 s .Lire moins >
Langue :
Anglais
Audience :
Internationale
Vulgarisation :
Non
Établissement(s) :
CNRS
Université de Lille
Université de Lille
Date de dépôt :
2020-06-08T14:10:40Z