Bayesian Inference for Partially Identified Models

Exploring the Limits of Limited Data
 
 
Chapman & Hall/CRC (Verlag)
  • 1. Auflage
  • |
  • erschienen am 1. April 2015
  • |
  • 196 Seiten
 
E-Book | PDF mit Adobe DRM | Systemvoraussetzungen
978-1-4398-6940-6 (ISBN)
 
Bayesian Inference for Partially Identified Models: Exploring the Limits of Limited Data shows how the Bayesian approach to inference is applicable to partially identified models (PIMs) and examines the performance of Bayesian procedures in partially identified contexts. Drawing on his many years of research in this area, the author presents a thorough overview of the statistical theory, properties, and applications of PIMs.The book first describes how reparameterization can assist in computing posterior quantities and providing insight into the properties of Bayesian estimators. It next compares partial identification and model misspecification, discussing which is the lesser of the two evils. The author then works through PIM examples in depth, examining the ramifications of partial identification in terms of how inferences change and the extent to which they sharpen as more data accumulate. He also explains how to characterize the value of information obtained from data in a partially identified context and explores some recent applications of PIMs. In the final chapter, the author shares his thoughts on the past and present state of research on partial identification.This book helps readers understand how to use Bayesian methods for analyzing PIMs. Readers will recognize under what circumstances a posterior distribution on a target parameter will be usefully narrow versus uselessly wide.
  • Englisch
  • London
  • |
  • Großbritannien
Taylor & Francis Ltd
  • Für höhere Schule und Studium
  • |
  • Researchers and graduate students in statistics, biostatistics, epidemiology, the social sciences, and econometrics.
  • 45 s/w Abbildungen, 2 s/w Tabellen
  • |
  • 45 b/w images and 2 tables
  • 3,64 MB
978-1-4398-6940-6 (9781439869406)
1439869405 (1439869405)
weitere Ausgaben werden ermittelt
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.
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
"... 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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