Titre :

Adaptive mixtures of regressions: Improving predictive inference when population has changed

Auteur(s) :

Bouveyron, Charles [Auteur]
Statistique, Analyse et Modélisation Multidisciplinaire (SAmos-Marin Mersenne) [SAMM]
Jacques, Julien [Auteur]
Laboratoire Paul Painlevé - UMR 8524 [LPP]

Titre de la revue :

Communications in Statistics - Simulation and Computation

Pagination :

22

Éditeur :

Taylor & Francis

Date de publication :

2014

ISSN :

0361-0918

Mot(s)-clé(s) en anglais :

Transfer learning
Mixture of regressions
Switching regression
EM algorithm
Bayesian inference
MCMC algorithm
MCMC algorithm.

Discipline(s) HAL :

Mathématiques [math]/Statistiques [math.ST]
Statistiques [stat]/Théorie [stat.TH]

Résumé en anglais : [en]

The present work investigates the estimation of regression mixtures when population has changed between the training and the prediction stages. Two approaches are proposed: a parametric approach modelling the relationship ...
Lire la suite >The present work investigates the estimation of regression mixtures when population has changed between the training and the prediction stages. Two approaches are proposed: a parametric approach modelling the relationship between dependent variables of both populations, and a Bayesian approach in which the priors on the prediction population depend on the mixture regression parameters of the training population. The relevance of both approaches is illustrated on simulations and on an environmental dataset.Lire moins >

Langue :

Anglais

Vulgarisation :

Non

Collections :

• Laboratoire Paul Painlevé - UMR 8524

Source :

Harvested from HAL