Beyond Thresholding: Analysis and Improvements for Deterministic Parameter Estimation

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dc.creatorErkmen, Baris I.
dc.creatorGoyal, Vivek K.
dc.date2008-01-23
dc.date.accessioned2026-07-07T08:56:01Z
dc.date.available2026-07-07T08:56:01Z
dc.descriptionHard-threshold estimators are popular in signal processing applications. We provide a detailed study of using hard-threshold estimators for estimating an unknown deterministic signal when additive white Gaussian noise corrupts observations. The analysis, depending heavily on Cram{é}r-Rao bounds, motivates piecewise-linear estimation as a simple improvement to hard thresholding. We compare the performance of two piecewise-linear estimators to a hard-threshold estimator. When either piecewise-linear estimator is optimized for the decay rate of the basis coefficients, its performance is better than the best possible with hard thresholding.
dc.description18 pages, 11 figures
dc.identifierhttps://arxiv.org/abs/0801.3490
dc.identifierhttp://arxiv.org/abs/0801.3490
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/146467
dc.subjectStatistics Theory
dc.titleBeyond Thresholding: Analysis and Improvements for Deterministic Parameter Estimation
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