Adaptive complexity regularization for linear inverse problems

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We tackle the problem of building adaptive estimation procedures for ill-posed inverse problems. For general regularization methods depending on tuning parameters, we construct a penalized method that selects the optimal smoothing sequence without prior knowledge of the regularity of the function to be estimated. We provide for such estimators oracle inequalities and optimal rates of convergence. This penalized approach is applied to Tikhonov regularization and to regularization by projection.
Published in at http://dx.doi.org/10.1214/07-EJS115 the Electronic Journal of Statistics (http://www.i-journals.org/ejs/) by the Institute of Mathematical Statistics (http://www.imstat.org)

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Statistics Theory, 62G05; 34K29 (Primary)

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http://salesiana.dossiersoluciones.com/handle/123456789/166064

Consulte el texto completo en el siguiente enlace:

https://arxiv.org/abs/0807.4859
 http://arxiv.org/abs/0807.4859
 Electronic Journal of Statistics 2008, Vol. 2, 661-677
 doi:10.1214/07-EJS115

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arXiv