Bayesian Non- and Semi-parametric Methods and Applications /

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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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Detalles Bibliográficos
Autor principal: Rossi, Peter E. (Peter Eric), 1955- (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Princeton : Princeton University Press, [2014]
Colección:Book collections on Project MUSE.
Temas:
Ekonometri.
Economics, Mathematical.
Econometrics.
Bayesian statistical decision theory.
BUSINESS & ECONOMICS > Reference.
BUSINESS & ECONOMICS > Economics > General.
Theorie de la decision bayesienne.
Économetrie.
Electronic books.
Acceso en línea:Texto completo
Tabla de Contenidos:
  • 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
  • \
  • 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.
  • 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.
  • 4.1. Semi-parametric Regression with DP Priors
  • 4.2. Semi-parametric IV Models.
  • 5.1. Introduction
  • 5.2. Semi-parametric Random Coefficient Logit Models
  • 5.3. An Empirical Example of a Semi-parametric Random Coefficient Logit Model.
  • 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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