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- Hájek–Le_Cam_convolution_theorem abstract "In statistics, the Hájek–Le Cam convolution theorem states that any regular estimator in a parametric model is asymptotically equivalent to a sum of two independent random variables, one of which is normal with asymptotic variance equal to the inverse of Fisher information, and the other having arbitrary distribution.The obvious corollary from this theorem is that the “best” among regular estimators are those with the second component identically equal to zero. Such estimators are called efficient and are known to always exist for regular parametric models.The theorem is named after Jaroslav Hájek and Lucien Le Cam.".
- Hájek–Le_Cam_convolution_theorem wikiPageID "24198544".
- Hájek–Le_Cam_convolution_theorem wikiPageRevisionID "569827324".
- Hájek–Le_Cam_convolution_theorem subject Category:Statistical_theorems.
- Hájek–Le_Cam_convolution_theorem comment "In statistics, the Hájek–Le Cam convolution theorem states that any regular estimator in a parametric model is asymptotically equivalent to a sum of two independent random variables, one of which is normal with asymptotic variance equal to the inverse of Fisher information, and the other having arbitrary distribution.The obvious corollary from this theorem is that the “best” among regular estimators are those with the second component identically equal to zero.".
- Hájek–Le_Cam_convolution_theorem label "Hájek–Le Cam convolution theorem".
- Hájek–Le_Cam_convolution_theorem sameAs H%C3%A1jek%E2%80%93Le_Cam_convolution_theorem.
- Hájek–Le_Cam_convolution_theorem sameAs Q5962990.
- Hájek–Le_Cam_convolution_theorem sameAs Q5962990.
- Hájek–Le_Cam_convolution_theorem wasDerivedFrom Hájek–Le_Cam_convolution_theorem?oldid=569827324.