منو
رکورد قبلیرکورد بعدی

" Bayesian non- and semi-parametric methods and applications / "


Document Type : BL
Record Number : 1044530
Doc. No : b798900
Main Entry : Reyment, R. A.
Title & Author : Applied factor analysis in the natural sciences /\ Richard A. Reyment, K.G. Jöreskog ; appendix by Leslie F. Marcus.
Edition Statement : 1st pbk. ed.
Publication Statement : Cambridge [England] ;New York, NY, USA :: Cambridge University Press,, 1996.
Page. NO : 1 online resource (xiii, 202 pages) :: illustrations
ISBN : 1400850304
: : 9781400850303
: 0691145326
: 1306548020
: 9780691145327
: 9781306548021
Bibliographies/Indexes : Includes bibliographical references (pages 195-200) and index.
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.
Abstract : 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.
Subject : Bayesian statistical decision theory.
Subject : Econometrics.
Subject : Economics, Mathematical.
Subject : Bayesian statistical decision theory.
copyhttps://lib.clisel.com/site/catalogue/845086
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