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- W2019226008 abstract "We consider a deconvolution problem with a random noise. The noise is a product of a Gaussian stationary random process by a weight function with constant >0. We study the asymptotically minimax and Bayes settings . In the minimax model a priori information is given that the solution belongs to a ball in Sobolev space W2β(R1). For such a priori information we find an asymptotically minimax estimator of the solution. In the Bayes setting the noise is the same. The solution is a realization of a random process defined as a product of a Gaussian stationary random process by a weight function . We show that the standard Wiener filters remain asymptotically Bayes estimators for this modification of Wiener filtration. The introduction of weight functions is the main difference from the standard settings. This allows us not to make the traditional assumptions that the powers of noise and solutions are infinite or tend to infinity." @default.
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- W2019226008 date "2003-11-04" @default.
- W2019226008 modified "2023-10-02" @default.
- W2019226008 title "Asymptotically minimax and Bayes estimation in a deconvolution problem" @default.
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- W2019226008 doi "https://doi.org/10.1088/0266-5611/19/6/007" @default.
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