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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,...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Princeton, NJ :
Princeton University Press,
[2014]
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| Edición: | Course Book |
| Colección: | The Econometric and Tinbergen Institutes Lectures
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| Temas: | |
| Acceso en línea: | Texto completo Texto completo |
MARC
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| 100 | 1 | |a Rossi, Peter, |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 245 | 1 | 0 | |a Bayesian Non- and Semi-parametric Methods and Applications / |c Peter Rossi. |
| 250 | |a Course Book | ||
| 264 | 1 | |a Princeton, NJ : |b Princeton University Press, |c [2014] | |
| 264 | 4 | |c ©2014 | |
| 300 | |a 1 online resource (224 p.) : |b 66 line illus. | ||
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| 490 | 0 | |a The Econometric and Tinbergen Institutes Lectures | |
| 505 | 0 | 0 | |t Frontmatter -- |t Contents -- |t Preface -- |t 1. Mixtures of Normals -- |t 2. Dirichlet Process Prior and Density Estimation -- |t 3. Non-parametric Regression -- |t 4. Semi-parametric Approaches -- |t 5. Random Coefficient Models -- |t 6. Conclusions and Directions for Future Research -- |t Bibliography -- |t Index |
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| 520 | |a 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. | ||
| 538 | |a Mode of access: Internet via World Wide Web. | ||
| 546 | |a In English. | ||
| 588 | 0 | |a Description based on online resource; title from PDF title page (publisher's Web site, viewed 30. Aug 2021) | |
| 650 | 0 | |a Bayesian statistical decision theory. | |
| 650 | 0 | |a Econometrics. | |
| 650 | 0 | |a Economics, Mathematical. | |
| 650 | 7 | |a BUSINESS & ECONOMICS / Economics / Theory. |2 bisacsh | |
| 773 | 0 | 8 | |i Title is part of eBook package: |d De Gruyter |t Princeton University Press Complete eBook-Package 2014-2015 |z 9783110665925 |
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| 856 | 4 | 0 | |u https://degruyter.uam.elogim.com/isbn/9781400850303 |z Texto completo |
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