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Noise reduction by wavelet thresholding / Maarten JANSEN (Cop. 2001)
Titre : Noise reduction by wavelet thresholding Type de document : texte imprimé Auteurs : Maarten JANSEN, Auteur Editeur : Springer Verlag Année de publication : Cop. 2001 Collection : Lecture notes in statistics num. 161 Importance : XIX-191 p. ISBN/ISSN/EAN : 978-0-387-95244-4 Langues : Anglais Mots-clés : ondelette contrôle automatique bruit électronique méthode statistique technique digitale Résumé : This book discusses statistical applications of wavelet theory for use in signal and image processing. The emphasis is on smoothing by wavelet thresholding and extended methods. Wavelet thresholding is an example of non-linear and non-parametric smoothing. The first part discusses theoretical and practical issues concerned with minimum risk thresholding and fast threshold estimation, using generalized cross validation.
The extensions in later chapters consider possibilities to exploit three key properties of wavelets in statistics: sparsity, multiresolution, and locality. The author discusses original contributions to problems of correlated noise, scale dependent processing, Bayesian algorithms with geometrical priors (Markov random fields), non-equispaced data, and many other extensions.
The point of view lies on the bridge between statistics, signal and image processing, and approximation theory, and the book is accessible for researchers from all of these fields. Most of the material has in mind applications in signal or image processing, and signals and images are used extensively in the illustrations. Nevertheless, the algorithms are quite general in the sense that they could also serve in other regression problems. The book also pays attention to fast algorithms, and Matlab code reproducing many of the illustrations is available for free.Note de contenu : index, bibliogr. Noise reduction by wavelet thresholding [texte imprimé] / Maarten JANSEN, Auteur . - [S.l.] : Springer Verlag, Cop. 2001 . - XIX-191 p.. - (Lecture notes in statistics; 161) .
ISBN : 978-0-387-95244-4
Langues : Anglais
Mots-clés : ondelette contrôle automatique bruit électronique méthode statistique technique digitale Résumé : This book discusses statistical applications of wavelet theory for use in signal and image processing. The emphasis is on smoothing by wavelet thresholding and extended methods. Wavelet thresholding is an example of non-linear and non-parametric smoothing. The first part discusses theoretical and practical issues concerned with minimum risk thresholding and fast threshold estimation, using generalized cross validation.
The extensions in later chapters consider possibilities to exploit three key properties of wavelets in statistics: sparsity, multiresolution, and locality. The author discusses original contributions to problems of correlated noise, scale dependent processing, Bayesian algorithms with geometrical priors (Markov random fields), non-equispaced data, and many other extensions.
The point of view lies on the bridge between statistics, signal and image processing, and approximation theory, and the book is accessible for researchers from all of these fields. Most of the material has in mind applications in signal or image processing, and signals and images are used extensively in the illustrations. Nevertheless, the algorithms are quite general in the sense that they could also serve in other regression problems. The book also pays attention to fast algorithms, and Matlab code reproducing many of the illustrations is available for free.Note de contenu : index, bibliogr. Exemplaires
Code-barres Cote Support Localisation Section Disponibilité 17091 LNS 161 Livre Recherche Salle Disponible Non-standard rank tests / Arnold JANSSEN (Cop. 1990)
Titre : Non-standard rank tests Type de document : texte imprimé Auteurs : Arnold JANSSEN, Auteur ; David M. MASON, Auteur Editeur : Springer Verlag Année de publication : Cop. 1990 Collection : Lecture notes in statistics num. 65 Importance : 252 p ISBN/ISSN/EAN : 978-0-387-97484-2 Langues : Anglais Catégories : 62E20
62E30
62G10
62G20Mots-clés : statistique test de rang Résumé : This monograph extends the notion of locally most powerful rank tests to non-regular cases. Through this notion one is led in a natural way to "non-standard" rank tests. A nearly complete analysis of the finite sample and asymptotic properties of such rank tests is presented. Also an adaptive test procedure is proposed and studied, and the results of a Monte Carlo simulation are given which provide strong evidence that it should perform well in many practical situations. An appendix derives the limit experiments needed to investigate the asymptotic optimality of these "non-standard" rank tests under local alternatives. The results in the appendix should also be of separate interest. Note de contenu : index, références Non-standard rank tests [texte imprimé] / Arnold JANSSEN, Auteur ; David M. MASON, Auteur . - [S.l.] : Springer Verlag, Cop. 1990 . - 252 p. - (Lecture notes in statistics; 65) .
ISBN : 978-0-387-97484-2
Langues : Anglais
Catégories : 62E20
62E30
62G10
62G20Mots-clés : statistique test de rang Résumé : This monograph extends the notion of locally most powerful rank tests to non-regular cases. Through this notion one is led in a natural way to "non-standard" rank tests. A nearly complete analysis of the finite sample and asymptotic properties of such rank tests is presented. Also an adaptive test procedure is proposed and studied, and the results of a Monte Carlo simulation are given which provide strong evidence that it should perform well in many practical situations. An appendix derives the limit experiments needed to investigate the asymptotic optimality of these "non-standard" rank tests under local alternatives. The results in the appendix should also be of separate interest. Note de contenu : index, références Exemplaires
Code-barres Cote Support Localisation Section Disponibilité 7891 LNS 65 Livre Recherche Salle Disponible Nonlinear estimation and classification (Cop. 2003)
Titre : Nonlinear estimation and classification Type de document : texte imprimé Editeur : Springer Verlag Année de publication : Cop. 2003 Collection : Lecture notes in statistics num. 171 Importance : VII-474 p. ISBN/ISSN/EAN : 0-384-95471-6 Langues : Anglais Mots-clés : estimation nonlinéaire statistique Résumé : Researchers in many disciplines face the formidable task of analyzing massive amounts of high-dimensional and highly-structured data. This is due in part to recent advances in data collection and computing technologies. As a result, fundamental statistical research is being undertaken in a variety of different fields. Driven by the complexity of these new problems, and fueled by the explosion of available computer power, highly adaptive, non-linear procedures are now essential components of modern "data analysis," a term that we liberally interpret to include speech and pattern recognition, classification, data compression and signal processing. The development of new, flexible methods combines advances from many sources, including approximation theory, numerical analysis, machine learning, signal processing and statistics. The proposed workshop intends to bring together eminent experts from these fields in order to exchange ideas and forge directions for the future. Note de contenu : références Nonlinear estimation and classification [texte imprimé] . - [S.l.] : Springer Verlag, Cop. 2003 . - VII-474 p.. - (Lecture notes in statistics; 171) .
ISSN : 0-384-95471-6
Langues : Anglais
Mots-clés : estimation nonlinéaire statistique Résumé : Researchers in many disciplines face the formidable task of analyzing massive amounts of high-dimensional and highly-structured data. This is due in part to recent advances in data collection and computing technologies. As a result, fundamental statistical research is being undertaken in a variety of different fields. Driven by the complexity of these new problems, and fueled by the explosion of available computer power, highly adaptive, non-linear procedures are now essential components of modern "data analysis," a term that we liberally interpret to include speech and pattern recognition, classification, data compression and signal processing. The development of new, flexible methods combines advances from many sources, including approximation theory, numerical analysis, machine learning, signal processing and statistics. The proposed workshop intends to bring together eminent experts from these fields in order to exchange ideas and forge directions for the future. Note de contenu : références Exemplaires
Code-barres Cote Support Localisation Section Disponibilité 19139 LNS 171 Livre Recherche Salle Disponible Nonparametric goodness-of-fit testing under gaussian models / Yu I. INGSTER (Cop. 2003)
Titre : Nonparametric goodness-of-fit testing under gaussian models Type de document : texte imprimé Auteurs : Yu I. INGSTER, Auteur ; Irina A. SUSLINA, Auteur Editeur : Springer Verlag Année de publication : Cop. 2003 Collection : Lecture notes in statistics num. 169 Importance : XIV-452 p. ISBN/ISSN/EAN : 978-0-387-95531-5 Langues : Anglais Mots-clés : statistique nonparamétrique modèle gaussien Résumé : This book presents the modern theory of nonparametric goodness-of-fit testing. The study is based on an asymptotic version of the minimax approach. The methods for the construction of asymptotically optimal, rate optimal, and optimal adaptive test procedures are developed. The authors present many new results that demonstrate the principal differences between nonparametric goodness-of-fit testing problems with parametric goodness-of-fit testing problems and with non-parametric estimation problems. This book fills the gap in modern nonparametric statistical theory by discussing hypothesis testing.
The book is addressed to mathematical statisticians who are interesting in the theory of non-parametric statistical inference. It will be of interest to specialists who are dealing with applied non-parametric statistical problems that are relevant in signal detection and transmission and in technical and medical diagnostics among others.Note de contenu : index, références Nonparametric goodness-of-fit testing under gaussian models [texte imprimé] / Yu I. INGSTER, Auteur ; Irina A. SUSLINA, Auteur . - [S.l.] : Springer Verlag, Cop. 2003 . - XIV-452 p.. - (Lecture notes in statistics; 169) .
ISBN : 978-0-387-95531-5
Langues : Anglais
Mots-clés : statistique nonparamétrique modèle gaussien Résumé : This book presents the modern theory of nonparametric goodness-of-fit testing. The study is based on an asymptotic version of the minimax approach. The methods for the construction of asymptotically optimal, rate optimal, and optimal adaptive test procedures are developed. The authors present many new results that demonstrate the principal differences between nonparametric goodness-of-fit testing problems with parametric goodness-of-fit testing problems and with non-parametric estimation problems. This book fills the gap in modern nonparametric statistical theory by discussing hypothesis testing.
The book is addressed to mathematical statisticians who are interesting in the theory of non-parametric statistical inference. It will be of interest to specialists who are dealing with applied non-parametric statistical problems that are relevant in signal detection and transmission and in technical and medical diagnostics among others.Note de contenu : index, références Exemplaires
Code-barres Cote Support Localisation Section Disponibilité 19085 LNS 169 Livre Recherche Salle Disponible Nonparametric Monte Carlo tests and their applications / Lixing ZHU (Cop. 2005)
Titre : Nonparametric Monte Carlo tests and their applications Type de document : texte imprimé Auteurs : Lixing ZHU, Auteur Editeur : Springer Verlag Année de publication : Cop. 2005 Collection : Lecture notes in statistics num. 182 Importance : XI-181 p. ISBN/ISSN/EAN : 978-0-387-25038-0 Langues : Anglais Mots-clés : test de Monte Carlo Résumé : The author addresses both applied and theoretical aspects of nonparametric Monte Carlo tests. The new methodology has been used for model checking in many fields of statistics, such as multivariate distribution theory, parametric and semiparametric regression models, multivariate regression models, varying-coefficient models with longitudinal data, heteroscedasticity, and homogeneity of covariance matrices. This book will be of interest to both practitioners and researchers investigating goodness-of-fit tests and resampling approximations.
Every chapter of the book includes algorithms, simulations, and theoretical deductions. The prerequisites for a full appreciation of the book are a modest knowledge of mathematical statistics and limit theorems in probability/empirical process theory. The less mathematically sophisticated reader will find Chapters 1, 2 and 6 to be a comprehensible introduction on how and where the new method can apply and the rest of the book to be a valuable reference for Monte Carlo test approximation and goodness-of-fit tests.Note de contenu : index, références Nonparametric Monte Carlo tests and their applications [texte imprimé] / Lixing ZHU, Auteur . - [S.l.] : Springer Verlag, Cop. 2005 . - XI-181 p.. - (Lecture notes in statistics; 182) .
ISBN : 978-0-387-25038-0
Langues : Anglais
Mots-clés : test de Monte Carlo Résumé : The author addresses both applied and theoretical aspects of nonparametric Monte Carlo tests. The new methodology has been used for model checking in many fields of statistics, such as multivariate distribution theory, parametric and semiparametric regression models, multivariate regression models, varying-coefficient models with longitudinal data, heteroscedasticity, and homogeneity of covariance matrices. This book will be of interest to both practitioners and researchers investigating goodness-of-fit tests and resampling approximations.
Every chapter of the book includes algorithms, simulations, and theoretical deductions. The prerequisites for a full appreciation of the book are a modest knowledge of mathematical statistics and limit theorems in probability/empirical process theory. The less mathematically sophisticated reader will find Chapters 1, 2 and 6 to be a comprehensible introduction on how and where the new method can apply and the rest of the book to be a valuable reference for Monte Carlo test approximation and goodness-of-fit tests.Note de contenu : index, références Exemplaires
Code-barres Cote Support Localisation Section Disponibilité 20352 LNS 182 Livre Recherche Salle Disponible Parametric and nonparametric inference from record-breaking data / Sneth GULATI (Cop. 2003)
PermalinkProbability matchnig priors : higher order asymptotics / Gauri Sankar DATTA (Cop. 2004)
PermalinkRandom coefficient autoregressive models : an introduction / Des F. NICHOLLS (Cop. 1982)
PermalinkRandom effect and latent variable model selection / David B. DUNSON (Cop. 2008)
PermalinkRanked set sampling / Zehua CHEN (Cop. 2004)
PermalinkRobust and nonlinear time series analysis / J. FRANKE (Cop. 1984)
PermalinkSeries approximation methods in statistics / John E. KOLASSA (Cop. 1997)
PermalinkSpatial statistics and computational methods / Jesper MØLLER (Cop. 2003)
PermalinkStatistical matching / Susanne RÄSSLER (Cop. 2002)
PermalinkStatistics on special manifolds / Yasuko CHIKUSE (Cop. 2003)
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