Asymptotic equivalence for nonparametric regression with multivariate and random design

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dc.creatorReiß, Markus
dc.date2006-07-14
dc.date.accessioned2026-07-07T08:08:02Z
dc.date.available2026-07-07T08:08:02Z
dc.descriptionWe show that nonparametric regression is asymptotically equivalent in Le Cam's sense with a sequence of Gaussian white noise experiments as the number of observations tends to infinity. We propose a general constructive framework based on approximation spaces, which permits to achieve asymptotic equivalence even in the cases of multivariate and random design.
dc.description30 pages
dc.identifierhttps://arxiv.org/abs/math/0607342
dc.identifierhttp://arxiv.org/abs/math/0607342
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/131125
dc.subjectStatistics Theory
dc.subject62G08, 62G20, 62B15
dc.titleAsymptotic equivalence for nonparametric regression with multivariate and random design
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