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Gaillac, Christophe
and Gautier, Eric
(2019)
Adaptive estimation in the linear random coefficients model when regressors have limited variation.
TSE Working Paper, n. 19-1026, Toulouse
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Official URL : http://tse-fr.eu/pub/123181
Abstract
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.
| Item Type: | Monograph (Working Paper) |
|---|---|
| Language: | English |
| Date: | July 2019 |
| Place of Publication: | Toulouse |
| Subjects: | B- ECONOMIE ET FINANCE |
| Divisions: | TSE-R (Toulouse) |
| Institution: | Université Toulouse 1 Capitole |
| Site: | UT1 |
| Date Deposited: | 19 Jul 2019 14:07 |
| Last Modified: | 30 Aug 2022 09:55 |
| OAI Identifier: | oai:tse-fr.eu:123181 |
| URI: | https://publications.ut-capitole.fr/id/eprint/32633 |
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- Adaptive estimation in the linear random coefficients model when regressors have limited variation. (deposited 19 Jul 2019 14:07) [Currently Displayed]
