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,...
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| Main Author: | |
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| Format: | Electronic eBook |
| Language: | English |
| Published: |
Princeton :
Princeton University Press,
[2014]
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| Subjects: | |
| Online Access: |
Full text (MFA users only) |
| ISBN: | 9781400850303 1400850304 |
| Local Note: | ProQuest Ebook Central |
Table of Contents:
- 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.