False Precision in Bayesian Updating with Incomplete Models
作者:
Mitchell J. Small,
Paul S. Fischbeck,
期刊:
Human and Ecological Risk Assessment: An International Journal
(Taylor Available online 1999)
卷期:
Volume 5,
issue 2
页码: 291-304
ISSN:1080-7039
年代: 1999
DOI:10.1080/10807039991289446
出版商: TAYLOR & FRANCIS
数据来源: Taylor
摘要:
As risk analysts learn and use more advanced statistical methods for characterizing uncertainty in their assessments, care must be taken to avoid systematic errors in model specification and subsequent inference. We argue that misspecifcation of the likelihood function in Bayesian analysis, due to underestimated errors, failure to account for correlations in model-data errors, and failure to consider omitted confounding variables, is a particularly pervasive and difficult problem with potentially serious consequences. An illustrative example with an idealized exposure-risk model is used to demonstrate how such errors can lead to false precision - posterior estimates that appear precise but are in fact inaccurate. Initial guidance is suggested for considering the sensitivity of model results to these types of errors.
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