Wavelet-based estimation with multiple sampling rates Hall, Peter en_US Penev, Spiridon en_US 2021-11-25T13:41:14Z 2021-11-25T13:41:14Z 2004 en_US
dc.description.abstract We suggest an adaptive sampling rule for obtaining information from noisy signals using wavelet methods. The technique involves increasing the sampling rate when relatively high-frequency terms are incorporated into the wavelet estimator, and decreasing it when, again using thresholded terms as an empirical guide, signal complexity is judged to have decreased. Through sampling in this way the algorithm is able to accurately recover relatively complex signals without increasing the long-run average expense of sampling. It achieves this level of performance by exploiting the opportunities for near-real time sampling that are available if one uses a relatively high primary resolution level when constructing the basic wavelet estimator. In the practical problems that motivate the work, where signal to noise ratio is particularly high and the long-run average sampling rate may be several hundred thousand operations per second, high primary resolution levels are quite feasible. en_US
dc.identifier.issn 0090-5364 en_US
dc.language English
dc.language.iso EN en_US
dc.rights CC BY-NC-ND 3.0 en_US
dc.rights.uri en_US
dc.source Legacy MARC en_US
dc.title Wavelet-based estimation with multiple sampling rates en_US
dc.type Journal Article en
dcterms.accessRights metadata only access
dspace.entity.type Publication en_US
unsw.identifier.doiPublisher en_US
unsw.relation.faculty Science
unsw.relation.ispartofissue 5 en_US
unsw.relation.ispartofjournal Annals of Statistics en_US
unsw.relation.ispartofpagefrompageto 1933-1956 en_US
unsw.relation.ispartofvolume 32 en_US
unsw.relation.originalPublicationAffiliation Hall, Peter en_US
unsw.relation.originalPublicationAffiliation Penev, Spiridon, Mathematics & Statistics, Faculty of Science, UNSW en_US School of Mathematics & Statistics *
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