On minimax rates of convergence in image models under sequential design
AbstractA binary image model is studied with a Lipschitz edge function. The indicator function of the image is observed in random noise at n design points that can be chosen sequentially. The asymptotically minimax rate as n-->[infinity] is found in estimating the edge function, and an asymptotically optimal algorithm is described.
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Bibliographic InfoArticle provided by Elsevier in its journal Statistics & Probability Letters.
Volume (Year): 43 (1999)
Issue (Month): 4 (July)
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Web page: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description
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- Kim, Jae-Chun & Korostelev, Alexander, 2000. "Rates of convergence for the sup-norm risk in image models under sequential designs," Statistics & Probability Letters, Elsevier, vol. 46(4), pages 391-399, February.
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