Borrow it
Coverart for item
The Resource Applied Bayesian modeling and causal inference from incomplete-data perspectives : an essential journey with Donald Rubin's statistical family, edited by Andrew Gelman, Xiao-Li Meng

Applied Bayesian modeling and causal inference from incomplete-data perspectives : an essential journey with Donald Rubin's statistical family, edited by Andrew Gelman, Xiao-Li Meng

Contributor
Summary
  • "This book is a collection of articles from leading researchers on statistical methods relating to missing data analysis, causal inference, and statistical modeling, including multiple imputation, propensity scores, instrumental variables, and Bayesian inference. The book is dedicated to Professor Donald Rubin, on the occasion of his 60th birthday, in recognition of his many and wide-ranging contributions to statistics, particularly to the topic of statistical analysis with missing data."
  • "Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives presents an overview with applied examples of these key topics suitable for researchers in all areas of statistics. It adopts a practical approach suitable for applied statisticians working in social and political sciences, biological and medical sciences, and physical sciences, as well as graduate students of statistics and biostatistics."--Jacket
Language
eng
Work
Publication
Extent
xix, 407 pages
Contents
  • Rajeev Dehejia
  • 4.
  • Medication cost sharing and drug spending in Medicare
  • Alyce S. Adams
  • 5.
  • A comparison of experimental and observational data analyses
  • Jennifer L. Hill, Jerome P. Reiter and Elaine L. Zanutto
  • 6.
  • Fixing broken experiments using the propensity score
  • Bruce Sacerdote
  • 1.
  • 7.
  • The propensity score with continuous treatments
  • Keisuke Hirano and Guido W. Imbens
  • 8.
  • Causal inference with instrumental variables
  • Junni L. Zhang
  • 9.
  • Principal stratification
  • Constantine E. Frangakis
  • An overview of methods for causal inference from observational studies
  • Sander Greenland
  • 2.
  • Matching in observational studies
  • Paul R. Rosenbaum
  • 3.
  • Estimating causal effects in nonexperimental studies
Isbn
9780470090435
Instance
Label
Applied Bayesian modeling and causal inference from incomplete-data perspectives : an essential journey with Donald Rubin's statistical family
Title
Applied Bayesian modeling and causal inference from incomplete-data perspectives
Title remainder
an essential journey with Donald Rubin's statistical family
Statement of responsibility
edited by Andrew Gelman, Xiao-Li Meng
Contributor
Subject
Genre
Language
eng
Summary
  • "This book is a collection of articles from leading researchers on statistical methods relating to missing data analysis, causal inference, and statistical modeling, including multiple imputation, propensity scores, instrumental variables, and Bayesian inference. The book is dedicated to Professor Donald Rubin, on the occasion of his 60th birthday, in recognition of his many and wide-ranging contributions to statistics, particularly to the topic of statistical analysis with missing data."
  • "Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives presents an overview with applied examples of these key topics suitable for researchers in all areas of statistics. It adopts a practical approach suitable for applied statisticians working in social and political sciences, biological and medical sciences, and physical sciences, as well as graduate students of statistics and biostatistics."--Jacket
Member of
Cataloging source
DLC
Dewey number
519.5/42
Illustrations
illustrations
Index
index present
LC call number
QA279.5
LC item number
.A57 2004
Literary form
non fiction
Nature of contents
bibliography
http://library.link/vocab/relatedWorkOrContributorName
  • Rubin, Donald B
  • Gelman, Andrew
  • Meng, Xiao-Li
Series statement
Wiley series in probability and statistics
http://library.link/vocab/subjectName
  • Bayesian statistical decision theory
  • Missing observations (Statistics)
  • Bayes Theorem
  • Models, Statistical
Label
Applied Bayesian modeling and causal inference from incomplete-data perspectives : an essential journey with Donald Rubin's statistical family, edited by Andrew Gelman, Xiao-Li Meng
Instantiates
Publication
Bibliography note
Includes bibliographical references (pages 361-400) and index
Carrier category
volume
Carrier category code
  • nc
Carrier MARC source
rdacarrier
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Contents
  • Rajeev Dehejia
  • 4.
  • Medication cost sharing and drug spending in Medicare
  • Alyce S. Adams
  • 5.
  • A comparison of experimental and observational data analyses
  • Jennifer L. Hill, Jerome P. Reiter and Elaine L. Zanutto
  • 6.
  • Fixing broken experiments using the propensity score
  • Bruce Sacerdote
  • 1.
  • 7.
  • The propensity score with continuous treatments
  • Keisuke Hirano and Guido W. Imbens
  • 8.
  • Causal inference with instrumental variables
  • Junni L. Zhang
  • 9.
  • Principal stratification
  • Constantine E. Frangakis
  • An overview of methods for causal inference from observational studies
  • Sander Greenland
  • 2.
  • Matching in observational studies
  • Paul R. Rosenbaum
  • 3.
  • Estimating causal effects in nonexperimental studies
Control code
56032881
Dimensions
24 cm
Extent
xix, 407 pages
Isbn
9780470090435
Isbn Type
(acid-free paper)
Lccn
2004053957
Media category
unmediated
Media MARC source
rdamedia
Media type code
  • n
Other physical details
illustrations
Label
Applied Bayesian modeling and causal inference from incomplete-data perspectives : an essential journey with Donald Rubin's statistical family, edited by Andrew Gelman, Xiao-Li Meng
Publication
Bibliography note
Includes bibliographical references (pages 361-400) and index
Carrier category
volume
Carrier category code
  • nc
Carrier MARC source
rdacarrier
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Contents
  • Rajeev Dehejia
  • 4.
  • Medication cost sharing and drug spending in Medicare
  • Alyce S. Adams
  • 5.
  • A comparison of experimental and observational data analyses
  • Jennifer L. Hill, Jerome P. Reiter and Elaine L. Zanutto
  • 6.
  • Fixing broken experiments using the propensity score
  • Bruce Sacerdote
  • 1.
  • 7.
  • The propensity score with continuous treatments
  • Keisuke Hirano and Guido W. Imbens
  • 8.
  • Causal inference with instrumental variables
  • Junni L. Zhang
  • 9.
  • Principal stratification
  • Constantine E. Frangakis
  • An overview of methods for causal inference from observational studies
  • Sander Greenland
  • 2.
  • Matching in observational studies
  • Paul R. Rosenbaum
  • 3.
  • Estimating causal effects in nonexperimental studies
Control code
56032881
Dimensions
24 cm
Extent
xix, 407 pages
Isbn
9780470090435
Isbn Type
(acid-free paper)
Lccn
2004053957
Media category
unmediated
Media MARC source
rdamedia
Media type code
  • n
Other physical details
illustrations

Subject

Genre

Member of

Library Locations

    • Ellis LibraryBorrow it
      1020 Lowry Street, Columbia, MO, 65201, US
      38.944491 -92.326012

Library Links

Structured data from the Bibframe namespace is licensed under the Creative Commons Attribution 4.0 International License by University of Missouri Libraries. Additional terms may apply to data associated with third party namespaces.
Processing Feedback ...