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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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Lenguaje: | eng |
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Princeton University Press
2014
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Acceso en línea: | http://cds.cern.ch/record/2150103 |
_version_ | 1780950448533929984 |
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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 |
id | cern-2150103 |
institution | Organización Europea para la Investigación Nuclear |
language | eng |
publishDate | 2014 |
publisher | Princeton University Press |
record_format | invenio |
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 |