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dc.contributor.authorAntoniadis, Anestisen
dc.contributor.authorPensky, M.en
dc.contributor.authorSapatinas, Theofanisen
dc.creatorAntoniadis, Anestisen
dc.creatorPensky, M.en
dc.creatorSapatinas, Theofanisen
dc.date.accessioned2019-12-02T10:33:35Z
dc.date.available2019-12-02T10:33:35Z
dc.date.issued2014
dc.identifier.issn1292-8100
dc.identifier.urihttp://gnosis.library.ucy.ac.cy/handle/7/56424
dc.description.abstractWe consider the nonparametric regression estimation problem of recovering an unknown response function f on the basis of spatially inhomogeneous data when the design points follow a known density g with a finite number of well-separated zeros. In particular, we consider two different cases: when g has zeros of a polynomial order and when g has zeros of an exponential order. These two cases correspond to moderate and severe data losses, respectively. We obtain asymptotic (as the sample size increases) minimax lower bounds for the L2-risk when f is assumed to belong to a Besov ball, and construct adaptive wavelet thresholding estimators of f that are asymptotically optimal (in the minimax sense) or near-optimal within a logarithmic factor (in the case of a zero of a polynomial order), over a wide range of Besov balls. The spatially inhomogeneous ill-posed problem that we investigate is inherently more difficult than spatially homogeneous ill-posed problems like, e.g., deconvolution. In particular, due to spatial irregularity, assessment of asymptotic minimax global convergence rates is a much harder task than the derivation of asymptotic minimax local convergence rates studied recently in the literature. Furthermore, the resulting estimators exhibit very different behavior and asymptotic minimax global convergence rates in comparison with the solution of spatially homogeneous ill-posed problems. For example, unlike in the deconvolution problem, the asymptotic minimax global convergence rates are greatly influenced not only by the extent of data loss but also by the degree of spatial homogeneity of f. Specifically, even if 1/g is non-integrable, one can recover f as well as in the case of an equispaced design (in terms of asymptotic minimax global convergence rates) when it is homogeneous enough since the estimator is "borrowing strength" in the areas where f is adequately sampled. © EDP Sciences, SMAI 2013.en
dc.sourceESAIM - Probability and Statisticsen
dc.source.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85006320940&doi=10.1051%2fps%2f2012024&partnerID=40&md5=48ac2ee6a684dd4037196b11641bd7ad
dc.subjectRegression analysisen
dc.subjectNon-parametric regressionen
dc.subjectNonparametric regressionen
dc.subjectOptimizationen
dc.subjectBanach spacesen
dc.subjectThresholdingen
dc.subjectWavelet estimationen
dc.subjectBesov spacesen
dc.subjectAdaptivityen
dc.subjectEuler equationsen
dc.subjectInhomogeneous dataen
dc.subjectMinimax estimationen
dc.subjectMinimax estimationsen
dc.subjectWavelet estimationsen
dc.titleNonparametric regression estimation based on spatially inhomogeneous data: Minimax global convergence rates and adaptivityen
dc.typeinfo:eu-repo/semantics/article
dc.identifier.doi10.1051/ps/2012024
dc.description.volume18
dc.description.startingpage1
dc.description.endingpage41
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 :4</p>en
dc.source.abbreviationESAIM Prob.Stat.en
dc.contributor.orcidSapatinas, Theofanis [0000-0002-6126-4654]


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