Bayesian Inference for Partially Identified Models : Exploring the Limits of Limited Data
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| Title: | Bayesian Inference for Partially Identified Models : Exploring the Limits of Limited Data |
|---|---|
| Description: | 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. |
| Authors: | Paul Gustafson |
| Resource Type: | eBook. |
| Subjects: | QA279.5 |
| Categories: | MATHEMATICS / Probability & Statistics / Bayesian Analysis, MATHEMATICS / Probability & Statistics / General |
| Database: | eBook Collection (EBSCOhost) |
| FullText | Links: – Type: ebook-pdf – Type: ebook-epub Text: Availability: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Bayesian Inference for Partially Identified Models : Exploring the Limits of Limited Data – Name: Abstract Label: Description Group: Ab Data: 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. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Paul+Gustafson%22">Paul Gustafson</searchLink> – Name: TypePub Label: Resource Type Group: TypPub Data: eBook. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22QA279%2E5%22">QA279.5</searchLink> – Name: SubjectBISAC Label: Categories Group: Su Data: <searchLink fieldCode="ZK" term="%22MATHEMATICS+%2F+Probability+%26+Statistics+%2F+Bayesian+Analysis%22">MATHEMATICS / Probability & Statistics / Bayesian Analysis</searchLink><br /><searchLink fieldCode="ZK" term="%22MATHEMATICS+%2F+Probability+%26+Statistics+%2F+General%22">MATHEMATICS / Probability & Statistics / General</searchLink> |
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| RecordInfo | BibRecord: BibEntity: Classifications: – Code: 519.542 Scheme: ddc Type: prePub Languages: – Code: eng Text: English Subjects: – SubjectFull: QA279.5 Type: general Titles: – TitleFull: Bayesian Inference for Partially Identified Models : Exploring the Limits of Limited Data Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Paul Gustafson – PersonEntity: Name: NameFull: Paul Gustafson IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Type: published Y: 2015 – D: 11 M: 04 Type: profile Y: 2015 Identifiers: – Type: isbn-print Value: 9781439869390 – Type: isbn-print Value: 9780367570538 – Type: isbn-print Value: 9780367240202 – Type: isbn-electronic Value: 9781439869406 – Type: isbn-electronic Value: 9780429192289 – Type: isbn-electronic Value: 9781040079560 Numbering: – Type: volume Value: 00141 Titles: – TitleFull: Bayesian Inference for Partially Identified Models : Exploring the Limits of Limited Data Type: main |
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