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Bayesian Inference for Partially Identified Models

New or Used: 31 copies from $122.49
New or Used: 31 copies from $122.49
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Table of Contents

Introduction Identification What Is against Us? What Is for Us? Some Simple Examples of Partially Identified Models The Road Ahead The Structure of Inference in Partially Identified Models Bayesian Inference The Structure of Posterior Distributions in PIMs Computational Strategies Strength of Bayesian Updating, Revisited Posterior Moments Credible Intervals Evaluating the Worth of Inference Partial Identification versus Model Misspecification The Siren Call of Identification Comparing Bias Reflecting Uncertainty A Further Example Other Investigations of PIM versus IPMM Models Involving Misclassification Binary to Trinary Misclassification Binary Misclassification across Three Populations Models Involving Instrumental Variables What Is an Instrumental Variable? Imperfect Compliance Modeling an Approximate Instrumental Variable Further Examples Inference in the Face of a Hidden Subpopulation Ecological Inference, Revisited Further Topics Computational Considerations Study Design Considerations Applications Concluding Thoughts What Have Others Said? What Is the Road ahead? Index

About the Author

Paul Gustafson is a professor in the Department of Statistics at the University of British Columbia. He is the statistics editor for Epidemiology as well as an associate editor for the Journal of the American Statistical Association (Applications and Case Studies Section) and Statistics in Medicine. His current research focuses on identification issues in Bayesian analysis.


"... In this little gem of a monograph, Paul Gustafson ... argues that partially identified models should not be so quickly dismissed. ... Gustafson has drawn together many discussions of identifiability from previous Bayesian analyses (including his own), which are not widely known in non-Bayesian circles. The writing is concise. The examples are simple and insightful. The reader need not be a Bayesian to appreciate this fine monograph." -Dale J. Poirier, University of California, Irvine, in Journal of the American Statistical Association, January 2017

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