Sensitivity analysis in statistical decision theory: A decision analytic view
- 1 April 1997
- journal article
- research article
- Published by Taylor & Francis in Journal of Statistical Computation and Simulation
- Vol. 57 (1) , 197-218
- https://doi.org/10.1080/00949659708811808
Abstract
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.Keywords
This publication has 11 references indexed in Scilit:
- Robustness issues under imprecise beliefs and preferencesJournal of Statistical Planning and Inference, 1994
- An overview of robust Bayesian analysisTEST, 1994
- Bayesian TheoryPublished by Wiley ,1994
- Infinitesimal sensitivity of posterior distributionsThe Canadian Journal of Statistics / La Revue Canadienne de Statistique, 1993
- Mathematical programming and the sensitivity of multi-criteria decisionsAnnals of Operations Research, 1993
- A framework for sensitivity analysis in discrete multi-objective decision-makingEuropean Journal of Operational Research, 1991
- Local Model InfluenceJournal of the American Statistical Association, 1989
- On the Consistency of Bayes EstimatesThe Annals of Statistics, 1986
- Statistical Decision Theory and Bayesian AnalysisPublished by Springer Nature ,1985
- A theory of requisite decision modelsActa Psychologica, 1984