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dc.contributor.authorAngelini, Claudiaen
dc.contributor.authorSapatinas, Theofanisen
dc.creatorAngelini, Claudiaen
dc.creatorSapatinas, Theofanisen
dc.date.accessioned2019-12-02T10:33:35Z
dc.date.available2019-12-02T10:33:35Z
dc.date.issued2004
dc.identifier.urihttp://gnosis.library.ucy.ac.cy/handle/7/56420
dc.description.abstractWe consider an empirical Bayes approach to standard nonparametric regression estimation using a nonlinear wavelet methodology. Instead of specifying a single prior distribution on the parameter space of wavelet coefficients, which is usually the case in the existing literature, we elicit the ε-contamination class of prior distributions that is particularly attractive to work with when one seeks robust priors in Bayesian analysis. The type II maximum likelihood approach to prior selection is used by maximizing the predictive distribution for the data in the wavelet domain over a suitable subclass of the ε-contamination class of prior distributions. For the prior selected, the posterior mean yields a thresholding procedure which depends on one free prior parameter and it is level- and amplitude-dependent, thus allowing better adaptation in function estimation. We consider an automatic choice of the free prior parameter, guided by considerations on an exact risk analysis and on the shape of the thresholding rule, enabling the resulting estimator to be fully automated in practice. We also compute pointwise Bayesian credible intervals for the resulting function estimate using a simulation-based approach. We use several simulated examples to illustrate the performance of the proposed empirical Bayes term-by-term wavelet scheme, and we make comparisons with other classical and empirical Bayes term-by-term wavelet schemes. As a practical illustration, we present an application to a real-life data set that was collected in an atomic force microscopy study.en
dc.sourceJournal of Statistical Computation and Simulationen
dc.source.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-3042709667&doi=10.1080%2f00949650310001643162&partnerID=40&md5=dc3b38ff95afa5973ec917ffe524efb0
dc.subjectε-Contaminated priorsel
dc.subjectNonparametric regressionen
dc.subjectAtomic force microscopyen
dc.subjectEmpirical Bayesen
dc.subjectWavelet transformen
dc.subjectExact risk analysisen
dc.subjectType-II maximum likelihood priorsen
dc.subjectWavelet shrinkage estimationen
dc.titleEmpirical bayes approach to wavelet regression using ε-contaminated priorsen
dc.typeinfo:eu-repo/semantics/article
dc.identifier.doi10.1080/00949650310001643162
dc.description.volume74
dc.description.issue10
dc.description.startingpage741
dc.description.endingpage764
dc.author.facultyΣχολή Θετικών και Εφαρμοσμένων Επιστημών / Faculty of Pure and Applied Sciences
dc.author.departmentΤμήμα Μαθηματικών και Στατιστικής / Department of Mathematics and Statistics
dc.type.uhtypeArticleen
dc.description.notes<p>Cited By :8</p>en
dc.source.abbreviationJ.Stat.Comput.Simul.en
dc.contributor.orcidAngelini, Claudia [0000-0001-8350-8464]
dc.contributor.orcidSapatinas, Theofanis [0000-0002-6126-4654]
dc.gnosis.orcid0000-0001-8350-8464
dc.gnosis.orcid0000-0002-6126-4654


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