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Adaptive estimation in the linear random coefficients model when regressors have limited variation

Gaillac, Christophe and Gautier, Eric (2021) Adaptive estimation in the linear random coefficients model when regressors have limited variation. Bernoulli journal.

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Official URL : http://tse-fr.eu/pub/125597
Identification Number : 10.1007/s00041-021-09875-6

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: 2021
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: 12 Oct 2021 10:49
OAI Identifier: oai:tse-fr.eu:125597
URI: http://publications.ut-capitole.fr/id/eprint/43542

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