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Gaillac, Christophe
and Gautier, Eric
(2022)
Adaptive estimation in the linear random coefficients model when regressors have limited variation.
Bernoulli journal, vol. 28 (n° 1).
pp. 504-524.
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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: | Article |
|---|---|
| Language: | English |
| Date: | February 2022 |
| Refereed: | Yes |
| Place of Publication: | London |
| Subjects: | B- ECONOMIE ET FINANCE |
| Divisions: | TSE-R (Toulouse) |
| Site: | UT1 |
| Date Deposited: | 12 Oct 2021 10:33 |
| Last Modified: | 30 Aug 2022 09:56 |
| OAI Identifier: | oai:tse-fr.eu:125597 |
| URI: | https://publications.ut-capitole.fr/id/eprint/43542 |
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Adaptive estimation in the linear random coefficients model when regressors have limited variation. (deposited 19 Jul 2019 14:07)
- Adaptive estimation in the linear random coefficients model when regressors have limited variation. (deposited 12 Oct 2021 10:33) [Currently Displayed]
