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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 :
Princeton University Press,
[2014]
|
| Colección: | Econometric and Tinbergen Institutes lectures.
|
| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 100 | 1 | |a Rossi, Peter E. |q (Peter Eric), |d 1955- |e author. | |
| 245 | 1 | 0 | |a Bayesian non- and semi-parametric methods and applications / |c Peter E. Rossi. |
| 264 | 1 | |a Princeton : |b Princeton University Press, |c [2014] | |
| 264 | 4 | |c ©2014 | |
| 300 | |a 1 online resource (xiii, 202 pages) : |b illustrations | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 490 | 1 | |a The econometric and tinbergen institutes lectures | |
| 504 | |a Includes bibliographical references (pages 195-200) and index. | ||
| 588 | 0 | |a Print version record. | |
| 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. | ||
| 505 | 0 | |a 1.1. Finite Mixture of Normals Likelihood Function -- 1.2. Maximum Likelihood Estimation -- 1.3. Bayesian Inference for the Mixture of Normals Model -- 1.4. Priors and the Bayesian Model -- 1.5. Unconstrained Gibbs Sampler -- 1.6. Label-Switching -- 1.7. Examples -- 1.8. Clustering Observations -- 1.9. Marginalized Samplers -- \ | |
| 505 | 0 | |a 2.1. Dirichlet Processes-A Construction -- 2.2. Finite and Infinite Mixture Models -- 2.3. Stick-Breaking Representation -- 2.4. Polya Urn Representation and Associated Gibbs Sampler -- 2.5. Priors on DP Parameters and Hyper-parameters -- 2.6. Gibbs Sampler for DP Models and Density Estimation -- 2.7. Scaling the Data -- 2.8. Density Estimation Examples. | |
| 505 | 0 | |a 3.1. Joint vs. Conditional Density Approaches -- 3.2. Implementing the Joint Approach with Mixtures of Normals -- 3.3. Examples of Non-parametric Regression Using Joint Approach -- 3.4. Discrete Dependent Variables -- 3.5. An Example of Expenditure Function Estimation. | |
| 505 | 0 | |a 4.1. Semi-parametric Regression with DP Priors -- 4.2. Semi-parametric IV Models. | |
| 505 | 0 | |a 5.1. Introduction -- 5.2. Semi-parametric Random Coefficient Logit Models -- 5.3. An Empirical Example of a Semi-parametric Random Coefficient Logit Model. | |
| 505 | 0 | |a 6.1. When Are Non-parametric and Semi-parametric Methods Most Useful? -- 6.2. Semi-parametric or Non-parametric Methods? -- 6.3. Extensions. | |
| 546 | |a English. | ||
| 590 | |a JSTOR |b Books at JSTOR All Purchased | ||
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| 590 | |a JSTOR |b Books at JSTOR Evidence Based Acquisitions | ||
| 650 | 0 | |a Econometrics. | |
| 650 | 0 | |a Bayesian statistical decision theory. | |
| 650 | 0 | |a Economics, Mathematical. | |
| 650 | 6 | |a Économétrie. | |
| 650 | 6 | |a Théorie de la décision bayésienne. | |
| 650 | 7 | |a BUSINESS & ECONOMICS |x Economics |x General. |2 bisacsh | |
| 650 | 7 | |a BUSINESS & ECONOMICS |x Reference. |2 bisacsh | |
| 650 | 7 | |a Bayesian statistical decision theory. |2 fast |0 (OCoLC)fst00829019 | |
| 650 | 7 | |a Econometrics. |2 fast |0 (OCoLC)fst00901574 | |
| 650 | 7 | |a Economics, Mathematical. |2 fast |0 (OCoLC)fst00902260 | |
| 650 | 7 | |a Ekonometri. |2 sao | |
| 776 | 0 | 8 | |i Print version: |a Rossi, Peter E. (Peter Eric), 1955- |t Bayesian non- and semi-parametric methods and applications |z 9780691145327 |w (DLC) 2013038609 |w (OCoLC)859168674 |
| 830 | 0 | |a Econometric and Tinbergen Institutes lectures. | |
| 856 | 4 | 0 | |u https://jstor.uam.elogim.com/stable/10.2307/j.ctt5hhrfp |z Texto completo |
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