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140404t20142014nju o 00 0 eng d |
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|a 9781400850303
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|z 9780691145327
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|a MdBmJHUP
|c MdBmJHUP
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100 |
1 |
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|a Rossi, Peter E.
|q (Peter Eric),
|d 1955-
|e author.
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245 |
1 |
0 |
|a Bayesian Non- and Semi-parametric Methods and Applications /
|c Peter E. Rossi.
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264 |
|
1 |
|a Princeton :
|b Princeton University Press,
|c [2014]
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264 |
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3 |
|a Baltimore, Md. :
|b Project MUSE,
|c 0000
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264 |
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4 |
|c ©[2014]
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300 |
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|a 1 online resource:
|b illustrations
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336 |
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|a text
|b txt
|2 rdacontent
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337 |
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|a computer
|b c
|2 rdamedia
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338 |
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|a online resource
|b cr
|2 rdacarrier
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490 |
0 |
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|a The econometric and tinbergen institutes lectures
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505 |
0 |
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|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 -- \
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505 |
0 |
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|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.
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505 |
0 |
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|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.
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505 |
0 |
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|a 4.1. Semi-parametric Regression with DP Priors -- 4.2. Semi-parametric IV Models.
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505 |
0 |
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|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.
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505 |
0 |
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|a 6.1. When Are Non-parametric and Semi-parametric Methods Most Useful? -- 6.2. Semi-parametric or Non-parametric Methods? -- 6.3. Extensions.
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520 |
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|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.
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546 |
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|a English.
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588 |
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|a Description based on print version record.
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650 |
|
7 |
|a Ekonometri.
|2 sao
|
650 |
|
7 |
|a Economics, Mathematical.
|2 fast
|0 (OCoLC)fst00902260
|
650 |
|
7 |
|a Econometrics.
|2 fast
|0 (OCoLC)fst00901574
|
650 |
|
7 |
|a Bayesian statistical decision theory.
|2 fast
|0 (OCoLC)fst00829019
|
650 |
|
7 |
|a BUSINESS & ECONOMICS
|x Reference.
|2 bisacsh
|
650 |
|
7 |
|a BUSINESS & ECONOMICS
|x Economics
|x General.
|2 bisacsh
|
650 |
|
6 |
|a Theorie de la decision bayesienne.
|
650 |
|
6 |
|a Économetrie.
|
650 |
|
0 |
|a Economics, Mathematical.
|
650 |
|
0 |
|a Bayesian statistical decision theory.
|
650 |
|
0 |
|a Econometrics.
|
655 |
|
7 |
|a Electronic books.
|2 local
|
710 |
2 |
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|a Project Muse.
|e distributor
|
830 |
|
0 |
|a Book collections on Project MUSE.
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856 |
4 |
0 |
|z Texto completo
|u https://projectmuse.uam.elogim.com/book/41636/
|
945 |
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|a Project MUSE - Custom Collection
|