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
Dewey number
330.01/519542
Illustrations
illustrations
Index
index present
LC call number
HB139
LC item number
.R64 2014eb
Literary form
non fiction
Nature of contents
  • dictionaries
  • bibliography
Series statement
The econometric and tinbergen institutes lectures
Label
Bayesian non- and semi-parametric methods and applications, Peter E. Rossi
Publication
Copyright
Antecedent source
unknown
Bibliography note
Includes bibliographical references (pages [195]-200) and index
http://library.link/vocab/branchCode
  • net
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
Control code
ocn875686973
Dimensions
unknown
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
http://library.link/vocab/ext/overdrive/overdriveId
  • 22573/ctt5dd1vp
  • 586053
Quality assurance targets
not applicable
http://library.link/vocab/recordID
.b30239436
Reformatting quality
unknown
Sound
unknown sound
Specific material designation
remote
System control number
  • (OCoLC)875686973
  • pebcs1400850304

Library Locations

    • Deakin University Library - Geelong Waurn Ponds CampusBorrow it
      75 Pigdons Road, Waurn Ponds, Victoria, 3216, AU
      -38.195656 144.304955
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