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Bayesian Inference for Partially Identified Models
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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.

Reviews

"... 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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