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  ▼a  875634905
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  ▼a  9781400850303
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• 100

  ▼a  Rossi, Peter E.
  ▼q  (Peter Eric),
  ▼d  1955-,
  ▼e  author.

• 245

  ▼a  Bayesian non- and semi-parametric methods and applications /:
  ▼c  Peter E. Rossi.

• 260

  ▼a  Princeton:
  ▼b  Princeton University Press,
  ▼c  [2014],
  ▼c  짤2014.

• 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

  ▼a  The econometric and tinbergen institutes lectures

• 504

  ▼a  Includes bibliographical references (pages 195-200) and index.

• 505

  ▼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

  ▼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

  ▼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

  ▼a  4.1. Semi-parametric Regression with DP Priors -- 4.2. Semi-parametric IV Models.

• 505

  ▼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

  ▼a  6.1. When Are Non-parametric and Semi-parametric Methods Most Useful? -- 6.2. Semi-parametric or Non-parametric Methods? -- 6.3. Extensions.

• 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.

• 588

  ▼a  Print version record.

• 590

  ▼a  eBooks on EBSCOhost
  ▼b  All EBSCO eBooks

• 650

  ▼a  Econometrics.

• 650

  ▼a  Bayesian statistical decision theory.

• 650

  ▼a  Economics, Mathematical.

• 650

  ▼a  BUSINESS & ECONOMICS
  ▼x  Economics
  ▼x  General.
  ▼2  bisacsh

• 650

  ▼a  BUSINESS & ECONOMICS
  ▼x  Reference.
  ▼2  bisacsh

• 650

  ▼a  Bayesian statistical decision theory.
  ▼2  fast
  ▼0  (OCoLC)fst00829019

• 650

  ▼a  Econometrics.
  ▼2  fast
  ▼0  (OCoLC)fst00901574

• 650

  ▼a  Economics, Mathematical.
  ▼2  fast
  ▼0  (OCoLC)fst00902260

• 655

  ▼a  Electronic books.

• 776

  ▼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

  ▼a  Econometric and Tinbergen Institutes lectures.

• 856

  ▼u  http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=681619

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  ▼a  Coutts Information Services
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  ▼a  ebrary
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  ▼n  ebr10853263

• 938

  ▼a  EBSCOhost
  ▼b  EBSC
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• 938

  ▼a  Ingram Digital eBook Collection
  ▼b  IDEB
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• 938

  ▼a  YBP Library Services
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• 990

  ▼a  강리원

• 991

  ▼a  eBook

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