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Bayesian non- and semi-parametric methods and applications

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...

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Detalles Bibliográficos
Autor principal: Rossi, Peter
Lenguaje:eng
Publicado: Princeton University Press 2014
Materias:
Acceso en línea:http://cds.cern.ch/record/2150103
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author Rossi, Peter
author_facet Rossi, Peter
author_sort Rossi, Peter
collection CERN
description 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
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institution Organización Europea para la Investigación Nuclear
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publishDate 2014
publisher Princeton University Press
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spelling cern-21501032021-04-21T19:43:02Zhttp://cds.cern.ch/record/2150103engRossi, PeterBayesian non- and semi-parametric methods and applicationsMathematical Physics and Mathematics 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 Princeton University Pressoai:cds.cern.ch:21501032014
spellingShingle Mathematical Physics and Mathematics
Rossi, Peter
Bayesian non- and semi-parametric methods and applications
title Bayesian non- and semi-parametric methods and applications
title_full Bayesian non- and semi-parametric methods and applications
title_fullStr Bayesian non- and semi-parametric methods and applications
title_full_unstemmed Bayesian non- and semi-parametric methods and applications
title_short Bayesian non- and semi-parametric methods and applications
title_sort bayesian non- and semi-parametric methods and applications
topic Mathematical Physics and Mathematics
url http://cds.cern.ch/record/2150103
work_keys_str_mv AT rossipeter bayesiannonandsemiparametricmethodsandapplications