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  • Search: subject:"Blind deconvolution"
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Year of publication
Subject
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Blind deconvolution 1 Blockwise SVD 1 Efficiency 1 Efficiency estimation 1 Effizienz 1 Estimation theory 1 Hibert scale 1 Linear inverse problems 1 Nichtparametrisches Verfahren 1 Nonparametric 1 Nonparametric adaptive estimation 1 Nonparametric statistics 1 Production function 1 Produktionsfunktion 1 Richardson-Lucy blind deconvolution 1 Schätztheorie 1 Second generation wavelets 1 Spherical deconvolution 1 Stochastic frontier estimation 1 Technical efficiency 1 Technische Effizienz 1 blind deconvolution 1 inverse problem 1
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Online availability
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Free 1 Undetermined 1
Type of publication
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Article 2 Book / Working Paper 1
Type of publication (narrower categories)
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Article in journal 1 Aufsatz in Zeitschrift 1
Language
All
English 2 Undetermined 1
Author
All
Dai, Xiaofeng 1 Johannes, Jan 1 Van Bellegem, Sébastien 1 Vanhems, Anne 1 Vareschi, T. 1
Institution
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Toulouse School of Economics (TSE) 1
Published in...
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European journal of operational research : EJOR 1 Journal of Multivariate Analysis 1 TSE Working Papers 1
Source
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RePEc 2 ECONIS (ZBW) 1
Showing 1 - 3 of 3
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Non-parametric efficiency estimation using Richardson-Lucy blind deconvolution
Dai, Xiaofeng - In: European journal of operational research : EJOR 248 (2016) 2, pp. 731-739
Persistent link: https://www.econbiz.de/10011409779
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Convergence Rates for III-Posed Inverse Problems with an Unknown Operator
Johannes, Jan; Van Bellegem, Sébastien; Vanhems, Anne - Toulouse School of Economics (TSE) - 2009
This paper studies the estimation of a nonparametric function ' from the inverse problem r = T' given estimates of the function r and of the linear transform T. The rate of convergence of the estimator is derived under two assumptions expressed in a Hilbert scale. The approach provides a unified...
Persistent link: https://www.econbiz.de/10008465389
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Application of second generation wavelets to blind spherical deconvolution
Vareschi, T. - In: Journal of Multivariate Analysis 124 (2014) C, pp. 398-417
We address the problem of spherical deconvolution in a non-parametric statistical framework, where both the signal and the operator kernel are subject to measurement errors. After a preliminary treatment of the kernel, we apply a thresholding procedure to the signal in a second generation...
Persistent link: https://www.econbiz.de/10011041959
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