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Bayesian and frequentist regression methods / Jon Wakefield (2013)  

Public Titre : Bayesian and frequentist regression methods Type de document : Monographie Auteurs : Jon Wakefield, Auteur Editeur : Berlin, Heidelberg, Vienne, New York, ... : Springer Année de publication : 2013 Importance : 697 p. Format : 16 x 24 cm ISBN/ISSN/EAN : 978-1-4419-0925-1 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Statistiques [Termes IGN] estimation bayesienne [Termes IGN] fonction spline [Termes IGN] inférence [Termes IGN] méthode du maximum de vraisemblance (estimation) [Termes IGN] méthode fondée sur le noyau [Termes IGN] modèle de régression [Termes IGN] modèle linéaire [Termes IGN] modèle non linéaire [Termes IGN] régression [Termes IGN] théorème de Bayes Résumé : (éditeur) Bayesian and Frequentist Regression Methods provides a modern account of both Bayesian and frequentist methods of regression analysis. Many texts cover one or the other of the approaches, but this is the most comprehensive combination of Bayesian and frequentist methods that exists in one place. The two philosophical approaches to regression methodology are featured here as complementary techniques, with theory and data analysis providing supplementary components of the discussion. In particular, methods are illustrated using a variety of data sets. The majority of the data sets are drawn from biostatistics but the techniques are generalizable to a wide range of other disciplines. While the philosophy behind each approach is discussed, the book is not ideological in nature and an emphasis is placed on practical application. It is shown that, in many situations, careful application of the respective approaches can lead to broadly similar conclusions. To use this text, the reader requires a basic understanding of calculus and linear algebra, and introductory courses in probability and statistical theory. The book is based on the author's experience teaching a graduate sequence in regression methods. The book website contains all of the code to reproduce all of the analyses and figures contained in the book. Note de contenu : 1- Inferential Approches 2- Independant data 3- Dependent data 4- Nonparametric modeling Numéro de notice : 25769 Affiliation des auteurs : non IGN Thématique : MATHEMATIQUE Nature : Monographie En ligne : https://doi.org/10.1007/978-1-4419-0925-1 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94957