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dc.contributor.authorAbramovich, F.en
dc.contributor.authorSapatinas, Theofanisen
dc.contributor.authorSilverman, B. W.en
dc.creatorAbramovich, F.en
dc.creatorSapatinas, Theofanisen
dc.creatorSilverman, B. W.en
dc.description.abstractWe consider random functions defined in terms of members of an overcomplete wavelet dictionary. The function is modelled as a sum of wavelet components at arbitrary positions and scales where the locations of the wavelet components and the magnitudes of their coefficients are chosen with respect to a marked Poisson process model. The relationships between the parameters of the model and the parameters of those Besov spaces within which realizations will fall are investigated. The models allow functions with specified regularity properties to be generated. They can potentially be used as priors in a Bayesian approach to curve estimation, extending current standard wavelet methods to be free from the dyadic positions and scales of the basis functions.en
dc.sourceProbability Theory and Related Fieldsen
dc.subjectContinuous wavelet transformen
dc.subjectBesov spacesen
dc.subjectOvercomplete wavelet dictionariesen
dc.subjectPoisson processesen
dc.titleStochastic expansions in an overcomplete wavelet dictionaryen
dc.description.endingpage144Σχολή Θετικών και Εφαρμοσμένων Επιστημών / Faculty of Pure and Applied SciencesΤμήμα Μαθηματικών και Στατιστικής / Department of Mathematics and Statistics
dc.description.notes<p>Cited By :10</p>en
dc.source.abbreviationProbab.Theory Relat.Fieldsen
dc.contributor.orcidSapatinas, Theofanis [0000-0002-6126-4654]

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