Mixing Least-Squares Estimators when the Variance is Unknown

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dc.creatorGiraud, Christophe
dc.date2007-11-02
dc.date.accessioned2026-07-07T08:40:16Z
dc.date.available2026-07-07T08:40:16Z
dc.descriptionWe propose a procedure to handle the problem of Gaussian regression when the variance is unknown. We mix least-squares estimators from various models according to a procedure inspired by that of Leung and Barron (2007). We show that in some cases the resulting estimator is a simple shrinkage estimator. We then apply this procedure in various statistical settings such as linear regression or adaptive estimation in Besov spaces. Our results provide non-asymptotic risk bounds for the Euclidean risk of the estimator.
dc.description30 pages
dc.identifierhttps://arxiv.org/abs/0711.0372
dc.identifierhttp://arxiv.org/abs/0711.0372
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/141326
dc.subjectStatistics Theory
dc.subject62G08
dc.titleMixing Least-Squares Estimators when the Variance is Unknown
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