Regression in random design and Bayesian warped wavelets estimators

Abstract : In this paper we deal with the regression problem in a random design setting. We investigate asymptotic optimality under minimax point of view of various Bayesian rules based on warped wavelets and show that they nearly attain optimal minimax rates of convergence over the Besov smoothness class considered. Warped wavelets have been introduced recently, they offer very good computable and easy-to-implement properties while being well adapted to the statistical problem at hand. We particularly put emphasis on Bayesian rules leaning on small and large variance Gaussian priors and discuss their simulation performances comparing them with a hard thresholding procedure.
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Electronic journal of statistics , Shaker Heights, OH : Institute of Mathematical Statistics, 2009, 3, pp.1084-1112
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Contributeur : Thanh Mai Pham Ngoc <>
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Dernière modification le : jeudi 27 avril 2017 - 09:46:33
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Thanh Mai Pham Ngoc. Regression in random design and Bayesian warped wavelets estimators. Electronic journal of statistics , Shaker Heights, OH : Institute of Mathematical Statistics, 2009, 3, pp.1084-1112. <hal-00347676v3>

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