Paul Gustafson

Bayesian Inference for Partially Identified Models

Exploring the Limits of Limited Data

• Gebundenes Buch

Produktbeschreibung

This book 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. He covers a range of PIMs, including models for misclassified data and models involving instrumental variables. He also includes real data applications of PIMs that have recently appeared in the literature.

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Produktdetails

• Verlag: CRC Press
• Seitenzahl: 196
• Erscheinungstermin: 1. April 2015
• Englisch
• Abmessung: 234mm x 157mm x 15mm
• Gewicht: 408g
• ISBN-13: 9781439869390
• ISBN-10: 1439869391
• Artikelnr.: 41620578

Autorenporträt

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.

Inhaltsangabe

Introduction. The Structure of Inference in Partially Identified Models.
Partial Identification versus Model Misspecification. Models Involving
Misclassification. Models Involving Instrumental Variables. Further
Examples. Further Topics. Concluding Thoughts. Index.

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Introduction. The Structure of Inference in Partially Identified Models. Partial Identification versus Model Misspecification. Models Involving Misclassification. Models Involving Instrumental Variables. Further Examples. Further Topics. Concluding Thoughts. Index.

Rezensionen

"... 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