Asymptotically efficient estimation of linear functionals in inverse regression models

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dc.creatorKlaassen, Chris A. J.
dc.creatorLee, Eun-Joo
dc.creatorRuymgaart, Frits H.
dc.date2002-12-27
dc.date.accessioned2026-07-07T08:06:05Z
dc.date.available2026-07-07T08:06:05Z
dc.descriptionIn this paper we will discuss a procedure to improve the usual estimator of a linear functional of the unknown regression function in inverse nonparametric regression models. In Klaassen, Lee, and Ruymgaart (2001) it has been proved that this traditional estimator is not asymptotically efficient (in the sense of the Hájek - Le Cam convolution theorem) except, possibly, when the error distribution is normal. Since this estimator, however, is still root-n consistent a procedure in Bickel, Klaassen, Ritov, and Wellner (1993) applies to construct a modification which is asymptotically efficient. A self-contained proof of the asymptotic efficiency is included.
dc.description14 pages
dc.identifierhttps://arxiv.org/abs/math/0212350
dc.identifierhttp://arxiv.org/abs/math/0212350
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/130484
dc.subjectStatistics Theory
dc.subject62G08; 62G20
dc.titleAsymptotically efficient estimation of linear functionals in inverse regression models
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