자료유형  

 단행본

Control Number  

n875686973

International Standard Book Number  

9781400850303 (electronic bk.)

International Standard Book Number  

1400850304 (electronic bk.)

International Standard Book Number  

9780691145327

International Standard Book Number  

0691145326

International Standard Book Number  

9781306548021

International Standard Book Number  

1306548020

Library of Congress Call Number  

HB139-.R64 2014eb

Dewey Decimal Classification Number  

330.01/519542-23

Main Entry-Personal Name  

Rossi, Peter E.((Peter Eric)) , 1955-

Publication, Distribution, etc. (Imprint  

Princeton : Princeton University Press, [2014]

Physical Description  

1 online resource (xiii, 202 pages) : illustrations.

Series Statement  

The econometric and tinbergen institutes lectures

Bibliography, Etc. Note  

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

Summary, Etc.  

요약This 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.

Formatted Contents Note  

완전내용1.1. 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 -- \

Formatted Contents Note  

완전내용2.1. 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.

Formatted Contents Note  

완전내용3.1. 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.

Formatted Contents Note  

완전내용4.1. Semi-parametric Regression with DP Priors -- 4.2. Semi-parametric IV Models.

Formatted Contents Note  

완전내용5.1. Introduction -- 5.2. Semi-parametric Random Coefficient Logit Models -- 5.3. An Empirical Example of a Semi-parametric Random Coefficient Logit Model.

Formatted Contents Note  

완전내용6.1. When Are Non-parametric and Semi-parametric Methods Most Useful? -- 6.2. Semi-parametric or Non-parametric Methods? -- 6.3. Extensions.

Subject Added Entry-Topical Term  

Econometrics

Subject Added Entry-Topical Term  

Bayesian statistical decision theory

Subject Added Entry-Topical Term  

Economics, Mathematical

Subject Added Entry-Topical Term  

BUSINESS & ECONOMICS Economics General.

Subject Added Entry-Topical Term  

BUSINESS & ECONOMICS Reference.

Subject Added Entry-Topical Term  

Bayesian statistical decision theory.

Subject Added Entry-Topical Term  

Econometrics.

Subject Added Entry-Topical Term  

Economics, Mathematical.

Additional Physical Form Entry  

Print version / Rossi, Peter E. (Peter Eric), 1955-Bayesian non- and semi-parametric methods and applications. 9780691145327. (DLC) 2013038609. (OCoLC)859168674

Series Added Entry-Uniform Title  

Econometric and Tinbergen Institutes lectures.

Electronic Location and Access  

로그인을 한후 보실 수 있는 자료입니다.

Control Number  

joongbu:504656