Asymptotic equivalence of nonparametric autoregression and nonparametric regression

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dc.creatorGrama, Ion G.
dc.creatorNeumann, Michael H.
dc.date2006-11-09
dc.date.accessioned2026-07-07T08:08:21Z
dc.date.available2026-07-07T08:08:21Z
dc.descriptionIt is proved that nonparametric autoregression is asymptotically equivalent in the sense of Le Cam's deficiency distance to nonparametric regression with random design as well as with regular nonrandom design.
dc.descriptionPublished at http://dx.doi.org/10.1214/009053606000000560 in the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)
dc.identifierhttps://arxiv.org/abs/math/0611257
dc.identifierhttp://arxiv.org/abs/math/0611257
dc.identifierAnnals of Statistics 2006, Vol. 34, No. 4, 1701-1732
dc.identifierdoi:10.1214/009053606000000560
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/131237
dc.subjectStatistics Theory
dc.subject62B15 (Primary) 62G07, 62G20 (Secondary)
dc.titleAsymptotic equivalence of nonparametric autoregression and nonparametric regression
dc.typetext

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