| 청구기호 |
HB139 .R64 2014eb |
| 형태사항 |
1 online resource (xiii, 202 pages) : illustrations.
|
| 언어 |
English |
| 서지주기 |
Includes bibliographical references (pages 195-200) and index.
|
| 내용 |
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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| 주제 |
Econometrics.
Bayesian statistical decision theory.
Economics, Mathematical.
BUSINESS & ECONOMICS --Economics --General. --bisacsh
BUSINESS & ECONOMICS --Reference. --bisacsh
Bayesian statistical decision theory. --fast --(OCoLC)fst00829019
Econometrics. --fast --(OCoLC)fst00901574
Economics, Mathematical. --fast --(OCoLC)fst00902260
|
| ISBN |
9781400850303q(electronic bk.)
1400850304 |
