Adaptive thresholding of sequences with locally variable strength |
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Authors: | T J Heaton |
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Institution: | (1) Department of Statistics, University of Oxford, 1 South Parks Road, Oxford, OX1 3TG, UK |
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Abstract: | This paper addresses, via thresholding, the estimation of a possibly sparse signal observed subject to Gaussian noise. Conceptually,
the optimal threshold for such problems depends upon the strength of the underlying signal. We propose two new methods that
aim to adapt to potential local variation in this signal strength and select a variable threshold accordingly. Our methods
are based upon an empirical Bayes approach with a smoothly variable mixing weight chosen via either spline or kernel based
marginal maximum likelihood regression. We demonstrate the excellent performance of our methods in both one and two-dimensional
estimation when compared to various alternative techniques. In addition, we consider the application to wavelet denoising
where reconstruction quality is significantly improved with local adaptivity. |
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Keywords: | Empirical Bayes Kernel smoothing Locally adaptive Spline smoothing Thresholding Wavelets |
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