Deconvolution Problems in Nonparametric Statistics - Couverture souple
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Synopsis
This book gives an introduction to deconvolution problems in nonparametric statistics. It details some real-life applications as well as methodology and theory.
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Présentation de l'éditeur
This book gives an introduction to deconvolution problems in nonparametric statistics. It details some real-life applications as well as methodology and theory.
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
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Deconvolution Problems in Nonparametric Statistics (eng)
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Deconvolution Problems in Nonparametric Statistics (Lecture Notes in Statistics, 193)
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Deconvolution Problems in Nonparametric Statistics (Lecture Notes in Statistics)
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Deconvolution Problems in Nonparametric Statistics
Edité par
Springer Berlin Heidelberg Mrz 2009, 2009
ISBN 10 : 3540875565
ISBN 13 : 9783540875567
Neuf
Taschenbuch
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Deconvolution problems occur in many elds of nonparametric statistics, for example, density estimation based on contaminated data, nonparametric - gression with errors-in-variables, image and signal deblurring. During the last two decades, those topics have received more and more attention. As appli- tions of deconvolution procedures concern many real-life problems in eco- metrics, biometrics, medical statistics, image reconstruction, one can realize an increasing number of applied statisticians who are interested in nonpa- metric deconvolution methods; on the other hand, some deep results from Fourier analysis, functional analysis, and probability theory are required to understand the construction of deconvolution techniques and their properties so that deconvolution is also particularly challenging for mathematicians. Thegeneraldeconvolutionprobleminstatisticscanbedescribedasfollows: Our goal is estimating a function f while any empirical access is restricted to some quantity h = f G = f(x y)dG(y), (1. 1) that is, the convolution of f and some probability distribution G. Therefore, f can be estimated from some observations only indirectly. The strategy is estimating h rst; this means producing an empirical version h of h and, then, applying a deconvolution procedure to h to estimate f. In the mathematical context, we have to invert the convolution operator with G where some reg- ularization is required to guarantee that h is contained in the invertibility domain of the convolution operator. The estimator h has to be chosen with respect to the speci c statistical experiment. 216 pp. Englisch. N° de réf. du vendeur 9783540875567
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Deconvolution Problems in Nonparametric Statistics
Edité par
Springer Berlin Heidelberg, 2009
ISBN 10 : 3540875565
ISBN 13 : 9783540875567
Neuf
Couverture souple
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Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Gives a general unifying approach to statistical deconvolution topics with easy to understand proofs and applicationsDeconvolution problems occur in many ?elds of nonparametric statistics, for example, density estimation based on contaminated data, nonp. N° de réf. du vendeur 4901669
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Deconvolution Problems in Nonparametric Statistics (Lecture Notes in Statistics)
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Springer, 2009
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ISBN 13 : 9783540875567
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Deconvolution Problems in Nonparametric Statistics
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Paperback. Etat : Brand New. 1st edition. 210 pages. 9.00x6.00x0.50 inches. In Stock. N° de réf. du vendeur x-3540875565
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Deconvolution Problems in Nonparametric Statistics
Edité par
Springer, Springer Vieweg Mär 2009, 2009
ISBN 10 : 3540875565
ISBN 13 : 9783540875567
Neuf
Taschenbuch
Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
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Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -Deconvolution problems occur in many elds of nonparametric statistics, for example, density estimation based on contaminated data, nonparametric - gression with errors-in-variables, image and signal deblurring. During the last two decades, those topics have received more and more attention. As appli- tions of deconvolution procedures concern many real-life problems in eco- metrics, biometrics, medical statistics, image reconstruction, one can realize an increasing number of applied statisticians who are interested in nonpa- metric deconvolution methods; on the other hand, some deep results from Fourier analysis, functional analysis, and probability theory are required to understand the construction of deconvolution techniques and their properties so that deconvolution is also particularly challenging for mathematicians. Thegeneraldeconvolutionprobleminstatisticscanbedescribedasfollo ws: Our goal is estimating a function f while any empirical access is restricted to some quantity h = f G = f(x y)dG(y), (1. 1) that is, the convolution of f and some probability distribution G. Therefore, f can be estimated from some observations only indirectly. The strategy is ¿ estimating h rst; this means producing an empirical version h of h and, then, ¿ applying a deconvolution procedure to h to estimate f. In the mathematical context, we have to invert the convolution operator with G where some reg- ¿ ularization is required to guarantee that h is contained in the invertibility ¿ domain of the convolution operator. The estimator h has to be chosen with respect to the speci c statistical experiment.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 216 pp. Englisch. N° de réf. du vendeur 9783540875567
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Deconvolution Problems in Nonparametric Statistics
Edité par
Springer, Springer Vieweg, 2009
ISBN 10 : 3540875565
ISBN 13 : 9783540875567
Neuf
Taschenbuch
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
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Taschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - Deconvolution problems occur in many elds of nonparametric statistics, for example, density estimation based on contaminated data, nonparametric - gression with errors-in-variables, image and signal deblurring. During the last two decades, those topics have received more and more attention. As appli- tions of deconvolution procedures concern many real-life problems in eco- metrics, biometrics, medical statistics, image reconstruction, one can realize an increasing number of applied statisticians who are interested in nonpa- metric deconvolution methods; on the other hand, some deep results from Fourier analysis, functional analysis, and probability theory are required to understand the construction of deconvolution techniques and their properties so that deconvolution is also particularly challenging for mathematicians. Thegeneraldeconvolutionprobleminstatisticscanbedescribedasfollows: Our goal is estimating a function f while any empirical access is restricted to some quantity h = f G = f(x y)dG(y), (1. 1) that is, the convolution of f and some probability distribution G. Therefore, f can be estimated from some observations only indirectly. The strategy is estimating h rst; this means producing an empirical version h of h and, then, applying a deconvolution procedure to h to estimate f. In the mathematical context, we have to invert the convolution operator with G where some reg- ularization is required to guarantee that h is contained in the invertibility domain of the convolution operator. The estimator h has to be chosen with respect to the speci c statistical experiment. N° de réf. du vendeur 9783540875567
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