Abstract
In order to obtain a good decision rule for some statistical problem we start by making assumptions concerning the class of distributions, the loss function, and other data of the problem. Usually these assumptions only approximate the actual conditions, either because the latter are unknown, or in order to simplify the mathematical treatment of the problem. Hence the assumptions under which a decision rule is derived are ordinarily not satisfied in a practical situation to which the rule is applied. It is therefore of interest to investigate how the performance of a decision rule is affected when the assumptions under which it was derived are replaced by another set of assumptions.
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Hoeffding, W. (1994). The Role of Assumptions in Statistical Decisions. In: Fisher, N.I., Sen, P.K. (eds) The Collected Works of Wassily Hoeffding. Springer Series in Statistics. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-0865-5_20
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DOI: https://doi.org/10.1007/978-1-4612-0865-5_20
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