Sensitivity analysis in statistical decision theory: A decision analytic view

Sensitivity analysis provides a way to mitigate traditional criticisms of Bayesian statistical decision theory, concerning dependence on subjective inputs. We suggest a general framework for sensitivity analysis allowing for perturbations in both the utility function and the prior distribution. Pert...

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Bibliographic Details
Published inJournal of statistical computation and simulation Vol. 57; no. 1-4; pp. 197 - 218
Main Authors Insua, David Rios, Martin, Jacinto, Proll, Les, French, Simon, Salhi, Abdellah
Format Journal Article
LanguageEnglish
Published Gordon and Breach Science Publishers 01.04.1997
Subjects
Bayesian statistics
classes of priors
classes of utilities
sensitivity analysis
statistical decision theory
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Summary:Sensitivity analysis provides a way to mitigate traditional criticisms of Bayesian statistical decision theory, concerning dependence on subjective inputs. We suggest a general framework for sensitivity analysis allowing for perturbations in both the utility function and the prior distribution. Perturbations are constrained to classes modelling imprecision in judgements The framework discards first definitely bad alternatives; then, identifies alternatives that may share optimality with a current one; and, finally, detects least changes in the inputs leading to changes in ranking. The associated computational problems and their implementation are discussed.
ISSN:0094-9655
1563-5163
DOI:10.1080/00949659708811808