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.
General
Imprint: |
Taylor & Francis
|
Country of origin: |
United States |
Series: |
Chapman & Hall/CRC Monographs on Statistics and Applied Probability |
Release date: |
April 2015 |
First published: |
2015 |
Authors: |
Paul Gustafson
|
Dimensions: |
234 x 156 x 16mm (L x W x T) |
Format: |
Hardcover
|
Pages: |
196 |
ISBN-13: |
978-1-4398-6939-0 |
Categories: |
Books >
Science & Mathematics >
Mathematics >
Probability & statistics
|
LSN: |
1-4398-6939-1 |
Barcode: |
9781439869390 |
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