Berry-Esseen bounds for wavelet estimator in a regression model with linear process errors
AbstractIn this paper, we derive the Berry-Esseen bounds of the wavelet estimator for a nonparametric regression model with linear process errors generated by [phi]-mixing sequences. As application, by the suitable choice of some constants, the convergence rate O(n-1/6) of uniformly asymptotic normality of the wavelet estimator is obtained. Our results generalize some known results in the literature.
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Bibliographic InfoArticle provided by Elsevier in its journal Statistics & Probability Letters.
Volume (Year): 81 (2011)
Issue (Month): 1 (January)
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Web page: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description
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- Yang, Shanchao, 2003. "Uniformly asymptotic normality of the regression weighted estimator for negatively associated samples," Statistics & Probability Letters, Elsevier, vol. 62(2), pages 101-110, April.
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