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The Resource Bayesian non- and semi-parametric methods and applications, Peter E. Rossi

Bayesian non- and semi-parametric methods and applications, Peter E. Rossi

Label
Bayesian non- and semi-parametric methods and applications
Title
Bayesian non- and semi-parametric methods and applications
Statement of responsibility
Peter E. Rossi
Creator
Author
Subject
Language
eng
Summary
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
Member of
Cataloging source
N$T
Illustrations
illustrations
Index
index present
Literary form
non fiction
Nature of contents
  • dictionaries
  • bibliography
Series statement
The econometric and tinbergen institutes lectures
Bayesian non- and semi-parametric methods and applications, Peter E. Rossi
Label
Bayesian non- and semi-parametric methods and applications, Peter E. Rossi
Link
http://www.jstor.org/stable/10.2307/j.ctt5hhrfp
Publication
Copyright
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Antecedent source
unknown
Bibliography note
Includes bibliographical references (pages 195-200) and index
Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
txt
Content type MARC source
rdacontent
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
http://library.link/vocab/cover_art
https://contentcafe2.btol.com/ContentCafe/Jacket.aspx?Return=1&Type=S&Value=9781400850303&userID=ebsco-test&password=ebsco-test
Dimensions
unknown
http://library.link/vocab/discovery_link
{'f': 'http://opac.lib.rpi.edu/record=b4331409'}
Extent
1 online resource (xiii, 202 pages)
File format
unknown
Form of item
online
Isbn
9781400850303
Level of compression
unknown
Media category
computer
Media MARC source
rdamedia
Media type code
c
Other physical details
illustrations.
Quality assurance targets
not applicable
Reformatting quality
unknown
Sound
unknown sound
Specific material designation
remote

Library Locations

    • Folsom LibraryBorrow it
      110 8th St, Troy, NY, 12180, US
      42.729766 -73.682577
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