Statistical Decisions under Ambiguity

@inproceedings{Stoye2007StatisticalDU,
  title={Statistical Decisions under Ambiguity},
  author={J{\"o}rg Stoye},
  year={2007}
}
Consider a decision maker who faces a number of possible models of the world. Every model generates objective probabilities, but no probabilities of models are given. This is the classic setting of statistical decision theory; recent and less standard applications include decision making with model uncertainty, e.g. due to concerns for misspecification, treatment choice with partial identification, and robust Bayesian analysis. I characterize a number of decision rules including Bayesianism… CONTINUE READING

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