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Combinatorial methods in density estimation / Luc Devroye

Public ISBD Titre : Combinatorial methods in density estimation Type de document : texte imprimé Auteurs : Luc Devroye, Auteur ; Gabor Lugosi, Auteur Editeur : Berlin : Springer Année de publication : 2001 Collection : Springer series in statistics Importance : 207 p. Présentation : ill. Format : 25 cm ISBN/ISSN/EAN : 978-0-387-95117-1 Note générale : Bibliogr. Index Langues : Anglais (eng) Mots-clés : Estimation  --  Additive  estimates  --  Théorie Analyse  combinatoire Index. décimale : 519.1 Analyse combinatoire. Théorie des graphies Résumé : Density estimation has evolved enormously since the days of bar plots and histograms, but researchers and users are still struggling with the problem of the selection of the bin widths. This text explores a new paradigm for the data-based or automatic selection of the free parameters of density estimates in general so that the expected error is within a given constant multiple of the best possible error. The paradigm can be used in nearly all density estimates and for most model selection problems, both parametric and nonparametric. It is the first book on this topic. The text is intended for first-year graduate students in statistics and learning theory, and offers a host of opportunities for further research and thesis topics. Each chapter corresponds roughly to one lecture, and is supplemented with many classroom exercises. A one year course in probability theory at the level of Feller's Volume 1 should be more than adequate preparation. Gabor Lugosi is Professor at Universitat Pompeu Fabra in Barcelona, and Luc Debroye is Professor at McGill University in Montreal. In 1996, the authors, together with Lászlo Györfi, published the successful text, A Probabilistic Theory of Pattern Recognition with Springer-Verlag. Both authors have made many contributions in the area of nonparametric estimation. Note de contenu : * Concentration Inequalities. * Uniform Deviation Inequalities. * Combinatorial Tools. * Total Variation. * Choosing a Density Estimate from a Collection. * Skeleton Estimates. * The Minimum Distance Estimate: Examples. * The Kernel Density Estimate. * Additive Estimates and Data Splitting. * Bandwidth Selection for Kernel Estimates. Combinatorial methods in density estimation [texte imprimé] / Luc Devroye, Auteur ; Gabor Lugosi, Auteur . - Springer, 2001 . - 207 p. : ill. ; 25 cm. - (Springer series in statistics) . ISBN : 978-0-387-95117-1 Bibliogr. Index Langues : Anglais (eng) Mots-clés : Estimation  --  Additive  estimates  --  Théorie Analyse  combinatoire Index. décimale : 519.1 Analyse combinatoire. Théorie des graphies Résumé : Density estimation has evolved enormously since the days of bar plots and histograms, but researchers and users are still struggling with the problem of the selection of the bin widths. This text explores a new paradigm for the data-based or automatic selection of the free parameters of density estimates in general so that the expected error is within a given constant multiple of the best possible error. The paradigm can be used in nearly all density estimates and for most model selection problems, both parametric and nonparametric. It is the first book on this topic. The text is intended for first-year graduate students in statistics and learning theory, and offers a host of opportunities for further research and thesis topics. Each chapter corresponds roughly to one lecture, and is supplemented with many classroom exercises. A one year course in probability theory at the level of Feller's Volume 1 should be more than adequate preparation. Gabor Lugosi is Professor at Universitat Pompeu Fabra in Barcelona, and Luc Debroye is Professor at McGill University in Montreal. In 1996, the authors, together with Lászlo Györfi, published the successful text, A Probabilistic Theory of Pattern Recognition with Springer-Verlag. Both authors have made many contributions in the area of nonparametric estimation. Note de contenu : * Concentration Inequalities. * Uniform Deviation Inequalities. * Combinatorial Tools. * Total Variation. * Choosing a Density Estimate from a Collection. * Skeleton Estimates. * The Minimum Distance Estimate: Examples. * The Kernel Density Estimate. * Additive Estimates and Data Splitting. * Bandwidth Selection for Kernel Estimates.

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Code-barres	Cote	Support	Localisation	Section	Disponibilité	Etat_Exemplaire
048061	519.1 DEV	Papier	Bibliothèque Centrale	Mathématiques	Disponible	En bon état
049341	519.1 DEV	Papier	Bibliothèque Centrale	Mathématiques	Disponible	En bon état