Fiche du document
2021
- ISIDORE Id: 10670/1.17d28b...
- hal: hal-03374805
- doi: 10.1007/s00041-021-09875-6
Ce document est lié à :
info:eu-repo/semantics/altIdentifier/doi/10.1007/s00041-021-09875-6
info:eu-repo/semantics/OpenAccess
Citer ce document
Christophe Gaillac et al., « Adaptive estimation in the linear random coefficients model when regressors have limited variation », HAL SHS (Sciences de l’Homme et de la Société), ID : 10.1007/s00041-021-09875-6
Métriques
Partage / Export
Résumé
We consider a linear model where the coecients - intercept and slopes - are random and independent from regressors which support is a proper subset. When the slopes do not have heavy tails, the joint density of the random coecients is identied. Lower bounds on the supremum risk for the estimation of the density are derived for this model and a related white noise model. We present an estimator, its rates of convergence, and a data-driven rule which delivers adaptive estimators.
