Bayesian non- and semi-parametric methods and applications

Les rabats de couverture indique : "This book reviews and develops Bayesian non-parametric and semi-parametric methods for applications in microeconometrics and quantitative marketing. Most econometric models used in microeconomics and marketing applications involve arbitrary distributional ass...

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Hlavní autor: Rossi, Peter E.
Médium: Livre numérique
Jazyk:Anglais
Vydáno: Princeton : Princeton University Press 2014.
Témata:
On-line přístup:Accès Université Orléans et IFPM
Poznámka: Couverture. https://static2.cyberlibris.com/books_upload/136pix/9781400850303.jpg
Titre provenant de la page de titre du document numérique
La pagination de l'édition imprimée correspondante est de 219 p.
Cyberlibris (ScholarVox) corpus sciences économiques et gestion
Autres localisations: Voir dans le Sudoc
Edition sous un autre format:• Bayesian non- and semi-parametric methods and applications, Peter E. Rossi, Princeton, Princeton University Press, 2014, 1 vol. (XIII- 202 p.), The Econometric and Tinbergen Institutes lectures, 978-0-691-14532-7
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245 1 0 |a Bayesian non- and semi-parametric methods and applications   |c Peter E. Rossi. 
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500 |a Titre provenant de la page de titre du document numérique 
500 |a La pagination de l'édition imprimée correspondante est de 219 p. 
500 |a Cyberlibris (ScholarVox) corpus sciences économiques et gestion 
504 |a Bibliogr. p. [195]-200. Index 
506 |a L'accès en ligne est réservé aux établissements ou bibliothèques ayant souscrit l'abonnement. Cyberlibris 
520 |a Les rabats de couverture indique : "This book reviews and develops Bayesian non-parametric and semi-parametric methods for applications in microeconometrics and quantitative marketing. Most econometric models used in microeconomics and marketing applications involve arbitrary distributional assumptions. As more data becomes available, a natural desire to provide methods that relax these assumptions arises. Peter Rossi advocates a Bayesian approach in which specific distributional assumptions are replaced with more flexible distributions based on mixtures of normals. The Bayesian approach can use either a large but fixed number of normal components in the mixture or an infinite number bounded only by the sample size. By using flexible distributional approximations instead of fixed parametric models, the Bayesian approach can reap the advantages of an efficient method that models all of the structure in the data while retaining desirable smoothing properties. Non-Bayesian non-parametric methods often require additional ad hoc rules to avoid "overfitting," in which resulting density approximates are nonsmooth. With proper priors, the Bayesian approach largely avoids overfitting, while retaining flexibility. This book provides methods for assessing informative priors that require only simple data normalizations. The book also applies the mixture of the normals approximation method to a number of important models in microeconometrics and marketing, including the non-parametric and semi-parametric regression models, instrumental variables problems, and models of heterogeneity. In addition, the author has written a free online software package in R, "bayesm," which implements all of the non-parametric models discussed in the book." 
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650 |a Modèles économétriques 
650 |a Statistique bayésienne 
650 |a Théorème de Bayes 
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