Browsing by Author "Pensky, M."
Now showing items 1-6 of 6
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Frequentist optimality of bayes factor estimators in wavelet regression models
Pensky, M.; Sapatinas, Theofanis (2007)We investigate the theoretical performance of Bayes factor estimators in wavelet regression models with independent and identically distributed errors that are not necessarily normally distributed. We compare these estimators ...
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Functional deconvolution in a periodic setting: Uniform case
Pensky, M.; Sapatinas, Theofanis (2009)We extend deconvolution in a periodic setting to deal with functional data. The resulting functional deconvolution model can be viewed as a generalization of a multitude of inverse problems in mathematical physics where ...
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Multichannel boxcar deconvolution with growing number of channels
Pensky, M.; Sapatinas, Theofanis (2011)We consider the problem of estimating the unknown response function in the multichannel deconvolution model with a boxcar-like ker-nel which is of particular interest in signal processing. It is known that, when the number ...
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Multichannel deconvolution with long-range dependence: A minimax study
Benhaddou, R.; Kulik, R.; Pensky, M.; Sapatinas, Theofanis (2014)We consider the problem of estimating the unknown response function in the multichannel deconvolution model with long-range dependent Gaussian or sub-Gaussian errors. We do not limit our consideration to a specific type ...
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Nonparametric regression estimation based on spatially inhomogeneous data: Minimax global convergence rates and adaptivity
Antoniadis, Anestis; Pensky, M.; Sapatinas, Theofanis (2014)We consider the nonparametric regression estimation problem of recovering an unknown response function f on the basis of spatially inhomogeneous data when the design points follow a known density g with a finite number of ...
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Article
On convergence rates equivalency and sampling strategies in functional deconvolution models
Pensky, M.; Sapatinas, Theofanis (2010)Using the asymptotical minimax framework, we examine convergence rates equivalency between a continuous functional deconvolution model and its real-life discrete counterpart over a wide range of Besov balls and for the ...